- All articles 719
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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
Preparing Mammograms for Classification Task: Processing and Analysis of Mammograms
Scientific article
Breast cancer is the most common cancer found in women in the world. Mammography has become indispensable for early detection of breast cancer. Radiologists interpret patients' mammograms by looking for some significant visual features for decision making. These features could have different interpretations based on expert's opinion and experience. Therefore, to solve the problem of different interpretations among experts, the use of computer in facilitating the processing and analysis of mammograms has become necessary. This study enhanced and segmented suspicious areas on mammograms obtained from Radiology Department, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. Also, Features were extracted from the segmented region of interests in order to prepare them for classification task. The result of implementation of enhancement algorithm used on mammograms shows all the subtle and obscure regions thereby making suspicious regions well visible which in turn helps in isolating the regions for extraction of textural features from them. Also, the result of the feature extraction shows pattern that will enable a classifier to classify these mammograms to one of normal, benign and malignant classes.
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Scientific article
In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.
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Scientific article
The present system performs analysis of snapshots of cursive and non-cursive font character text images and yields customizable text files using optical character recognition technology. In the previous versions the authors have discussed the user training mechanism that introduces new non-cursive font styles and writing formats into the system and incorporates optimization, noise reduction and background detection modules. This system specifically focuses on enhancing the process of character recognition by introducing a mechanism for handling simple cursive fonts.
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Scientific article
Technology Business Incubation (TBIs) has become a global phenomenon integral to the growth of regional innovation and startup ecosystems. The availability of high-quality infrastructure and facilities lays the foundations of the entire startup ecosystem for providing essential support services that directly impact entrepreneurial success. The incubation capacity of TBIs across different regions can foster competition and collaboration among these regions, provide avenues for enhancing enterprises’ incubation capabilities, and assist entrepreneurs in assessing the strength of regional incubation. However, with their rapid expansion, the performance evaluation also becomes increasingly complex due to the diversity of converging factors such as complex technologies, varying nature of relationships of VCs, and entrepreneurial competencies of the founders incubating startups at the TBIs. Traditional Machine Learning performance evaluation and prediction models struggle to capture these dynamic variables, while also suffering from privacy vulnerabilities, low accuracy, and reliance on centralized third parties. This often leads to single points of failure, performance bottlenecks, and sometimes increased costs. To address these challenges, we employed Privacy-Preserving Federated Learning with Blockchain (PPFL-BC), a novel framework designed for improving the mechanism of performance measurement and prediction for remote TBIs while ensuring that the privacy of entities and the data remains secure. We utilize capabilities of Artificial Neural Network (ANN) and gradient boosting-enabled federated learning to train the model of each TBI locally. In the process, no private and sensitive business data is shared outside the network, significantly reducing the risk of privacy breaches. Besides this, all the locally trained models are aggregated into a unified predictive model at the central aggregation unit, which ultimately improves the overall accuracy of the performance prediction mechanism for the entire population of TBIs. In our model, the decentralized blockchain network is also used to address security concerns related to unauthorized access and data manipulation thereby ensuring transparent and tamper-proof model updates. We evaluate the performance of our proposed PPFL-BC model by utilizing real-world business incubation datasets. The simulation results show that our model outperforms the centralized performance prediction models in terms of accuracy, precision, recall, and F1-score. The results show that the proposed PPFL-BC model outperforms benchmark models with an accuracy of 84% and precision of 0.92, which shows the efficiency and reliability of our model in predicting and validating TBI success rates.
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Problems of Regulation and Prospective Development of E-commerce Systems in the Post-coronavirus Era
Scientific article
The article examines the application of e-commerce systems and technologies that have a positive impact on the development of the economy of the post-coronavirus period and the formation of appropriate technical and technological infrastructure for it, as well as promising features and directions of e-commerce. The physical and virtual opportunities created by e-commerce technologies for buyers and sellers are explained. The advantages of e-commerce in the international economic space have been identified. The functions of e-business models in accordance with the commercial stages of enterprises are explained. It was noted that the development of ICT has accelerated the process of transition from traditional commerce to e-commerce, led to the emergence of new global trends in e-commerce. These innovations have raised the issue of the application of modern ICT in the development of e-commerce on the platform of the 4.0 Industrial Revolution. Taking into account these factors, the presented article discusses the application of modern technologies in e-commerce systems, such as 3D modeling, the Internet of Things, artificial intelligence, big data. Features of application and regulation mechanisms of E-commerce systems in real economic sectors, which have a direct stimulating effect on economic growth in Azerbaijan, have been studied. Recommendations were given for the modernization and use of e-commerce systems with the application of the latest ICT technologies.
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Scientific article
Abstract: Accurate profit forecasting is critical for small-scale pharmacies, particularly in resource-constrained environments where financial decisions must be both timely and data-informed. This study investigates the predictive performance of sixteen regression models for daily profit forecasting using transactional data collected from a single local pharmacy in Sabha, Libya, over a 14-month period. An exploratory data analysis revealed strong right-skewed distributions in sales, cost, and profit, as well as pronounced temporal patterns, including seasonal peaks during spring and early summer and weekly profit clustering around weekends. After outlier treatment using the interquartile range method. A total of sixteen regression models were developed and evaluated, encompassing linear models (Linear, Ridge, Lasso, ElasticNet), tree-based models (Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost), proximity-based models (K-Nearest Neighbors), kernel-based models (Support Vector Regression), and neural architectures (Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory, Gated Recurrent Unit). The models were assessed using Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and the R-squared score. The results consistently showed that tree-based ensemble models—particularly Extra Trees and LightGBM—achieved the highest accuracy, with R² values of 0.978 and 0.975 respectively, significantly outperforming neural and linear models. Learning curves and residual plots further confirmed the superior generalization and robustness of these models. We acknowledge that the dataset size (424 records) and the deterministic relationship between sales, costs, and profit influence these metrics. The study highlights the importance of model selection tailored to domain-specific data characteristics and suggests that well-tuned ensemble methods may offer reliable, interpretable, and scalable solutions for profit forecasting in simialr low-resource retail environments. However, broad claims of usefulness for all low-resource settings should be tempered by the limited scope of this dataset. Future work should consider longer-term data and external economic indicators to further improve model reliability, and focus on operational deployment strategies, investigating how these models can be integrated into daily pharmacy workflows despite real-time data constraints.
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Proposal of Enhanced Extreme Programming Model
Scientific article
Extreme programming is one of the commonly used agile methodologies in software development. It is very responsive to changing requirements even in the late phases of the project. However, quality activities in extreme programming phases are implemented sequentially along with the activities that work on the functional requirements. This reduces the agility to deliver increments continuously and makes an inverse relationship between quality and agility. Due to this relationship, extreme programming does not consume enough time on making extensive documentation and robust design. To overcome these issues, an enhanced extreme programming model is proposed. Enhanced extreme programming introduces parallelism in the activities' execution through putting quality activities into a separate execution line. In this way, the focus on delivering increments quickly is achieved without affecting the quality of the final output. In enhanced extreme programming, the quality concept is extended to include refinement of all phases of classical extreme programming and creating architectural design based on the refined design documents.
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Proposal to Decrease Code Defects to Improve Software Quality
Scientific article
Software quality is an important topic of software development and it is always challenging to deliver high-quality software. The major challenges, to complete the software, are time and cost without losing the software quality. Software quality has a significant impact on software performance. The acceptability, success, and failure of software are depending on its level of quality and number of defects. Software defects are one of the fundamental factors that can determine the time of software delivery. In addition, defects or errors need to be eliminated before software delivery. Software companies spend a lot to reduce code defects. The aim is to detect defects early with cheaper methods. This paper proposes a code quality scanner to decrease the code defects. The proposed solution is a combination of code scanner and code review. Moreover, the paper presents results using quantitative analysis to show the effectiveness of the proposed solution. The results are found encouraging.
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Quality Test Template toward Multi-user Access Control of Internet-Based System
Scientific article
Aiming at three kinds of Internet-based system quality problems, which is performance, liability and security, the paper proposes a kind of test template during multi-user login and resource access control, which includes test requirement, login script, role-resource correlating and mutation test technique. Some Internet-based systems are tested and diagnosed by automation test technique of test template. At last, system quality can be verified and improved through the realization mechanism of test template.
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Quality of Experience Assessment of Banking Service
Scientific article
In this paper, Quality of Experience (QoE) is used to assess and improve Bank’s customer satisfaction and provide quality of service (QoS) according to their demands. QoE based web platform was developed for the assessment of customer satisfaction. The Eclipse Neon Enterprise Edition was used for the design and development of platform and MySQL database was used for backend database storage. The front interface of the platform provided user facility to enter their complaints and information, which will store in the database. The stored data will be used for the analysis of a particular employee’s evaluations of his performance and behavior with customers. Management can observe the performance of the bank’s employees and can overcome their flaws by providing the required training. If one employee is lacking communication skills and is unable to convey his message to the customer of the bank, then the management can arrange training for improving his/her communication skills.
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Scientific article
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.
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Quantum Particle Swarm Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem
Scientific article
This paper presents a quantum behaved particle swarm algorithm for solving the multi-objective reactive power dispatch problem .Particle swarm optimization (PSO) is a population-based swarm intellect algorithm that share various similarities with evolutionary computation methods. Yet, PSO is determined by the imitation of a societal psychosomatic metaphor aggravated by cooperative behaviours of bird and other societal organisms instead of, the endurance of the fittest individual. Stimulated by the traditional PSO method and quantum procedure theories, this work presents a new Quantum behaved PSO (QPSO). The simulation results reveal high-quality performance of the QPSO in solving an optimal reactive power dispatch problem. In order to appraise the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
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Queuing Effect on Multipath Routing in Mobile Ad Hoc Networks
Scientific article
In Mobile Ad Hoc Network, data delivery is very challenging through single path due to dynamic changes in the network topology. To cope up this issue multipath data delivery is very useful. Recently, many works have been carried out in this domain but few are addressing the queueing effect on multipath scenarios. In this paper, we have designed a network model that based on the existence of multipath between source and destination node and every node behave as M/M/1 queue. In order to do this we generate K (K=1, 2, 3…i) paths are available between each source toward the destination node. The traffic arrivals in each node follow poisson process with arrival rate λ packets/sec. The simulation work of this multipath scenario based on varying mean inter-arrival time. The effect of arrival rate on the performance of multipath network model is analysed and compared. To better understand the effect of arrival rate in application and network layer various QoS metrics are computed. Significant performance of individual node is noticed in the obtained results with various arrival rates.
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RAFA-BioAuth: Risk-Adaptive and Fairness-Aware Biometric Authentication for Secure Mobile Banking
Scientific article
This research examines RAFA-BioAuth, a risk-adaptive, fairness-aware framework for mobile banking in cases of presentations and facial occlusions. The proposed solution combines aspects of: Identity Similarity, Passive Presentation Attack Detection (PAD), Asymmetric Financial-Risk Estimation and Fairness Regularization at the Identity Level. For evaluation purposes, all benchmark datasets were split into subject disjoint training, validation, and testing sets. The threshold values from the validation set were used. Bootstrap resampling was employed to estimate the variance. Monte Carlo simulations were performed to estimate the risk. The results showed that identity verification (AUC = 0.548) and PAD (AUC ≈ 0.55) were poor. Comparing RAFA to the AND rule resulted in FAR = 0.20, FRR = 0.31, and expected risk = 340. On the other hand, the AND rule had lower FAR of 0.09, but increased FRR to 0.78. Finally, a conservative end-to-end approach produced an FRR of 0.686. Thus, our results indicate trade-offs rather than production-readiness as we did not perform deployment, cross-device, or cross-dataset validations on our solutions. We present the contributions of this research as being an interpretable integration and not a new algorithm.
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Rank University Websites Using Fuzzy AHP and Fuzzy TOPSIS Approach on Usability
Scientific article
With the advent of dynamic website usually all business processes of a business organization are linked with the website of the organization. This is resulted in designing of a complex and gigantic website which may result in slow download and unfriendly navigation. Satisfying the end user need is one of the key principles of designing an effective website. As there are different users for given website, hence there are different criteria on which user wants to get satisfied, hence evaluating a website is a multi-criteria decision making problem. In order to incorporate uncertainties and vagueness in decision making Fuzzy Analytic Hierarchy (FAHP) approach is extended with Fuzzy TOPSIS approach, where different decision makers (DM's) opinion was considered for ranking the website.
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Real Time Speaker Recognition System for Hindi Words
Scientific article
Real time speaker recognition is needed for various voice controlled applications. Background noise influences the overall efficiency of speaker recognition system and is still considered a challenge in Speaker Recognition System (SRS). In this paper MFCC feature is used along with VQLBG algorithm for designing SRS. A new approach for designing a Voice Activity Detector (VAD) has been proposed which can discriminate between silence and voice activity and this can significantly improve the performance of SRS under noisy conditions. MFCC feature is extracted from the input speech and then vector quantization of the extracted MFCC features is done using VQLBG algorithm. Speaker identification is done by comparing the features of a newly recorded voice with the database under a specific threshold using Euclidean distance approach. The entire processing is done using MATLAB tool.The experimental result shows that the proposed method gives good results.
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Real estate recommendation using historical data and surrounding environments
Scientific article
Recommending appropriate things to the user by analyzing available data is becoming popular day by day. There are no sufficient researches on Real-estate recommendation with historical data and surrounding environments. We have collected real-estate, historical and point of interest (POI) data from the various sources. In this research, a hybrid filtering technique is used for recommending real-estate consisting of collaborative and content-based filtering. Generally, in every website user ratings are collected for the recommendation. But we have considered historical data and surrounding environments of a real-estate location for recommendation by which it will be easy for a user to decide that which place would be better for him/her. If any user request for any specific location then the system will find the POI data using google map API. Then the system will consider historical data of that area, got from the trusted sources. So considering the minimum price and optimal facilities, our system will recommend top-k real-estate. After extensive experiments on real and synthetic data, we have proved the efficiency of our proposed recommender system.
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Real-Time Obstacle Detection Approach using Stereoscopic Images
Scientific article
In this paper, we propose a new and simple approach to obstacle and free space detection in an indoor and outdoor environment in real-time using stereo vision as sensor. The real-time obstacle detection algorithm uses two dimensional disparity map to detect obstacles in the scene without constructing the ground plane. The proposed approach combines an accumulating and thresholding techniques to detect and cluster obstacle pixels into objects using a dense disparity map. The results from both analysis modules are combined to provide information of the free space. Experimental results are presented to show the effectiveness of the proposed method in real-time.
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Recommender System in Tourism Using Case based Reasoning Approach
Scientific article
Using recommender systems with the help of computer systems technology to support the Tourist advising process offers many advantages over the traditional system. A knowledge based recommender reasons about the fit between a user’s need and the features of available products. Providing an effective service in Ethiopian Tourism sector is critical to attract more foreign and local tourists. However, there are major problems that need immediate solution. First, the difficulty of getting fast, reliable, and consistent expert advice in the sector that is suitable to each visitor’s characteristics and capabilities. Second, inadequacy of the number of experienced experts and consulting individuals who can give advice on tourism issues in the country. Therefore, this paper aims to design a recommender system for tourist attraction area and visiting time selection that can assist experts and tourists to make timely decisions that helps them to get fast and consistent advisory service. So that, visitors can identify tourist attraction areas that have the highest potential of success/satisfaction and that match their personal characteristics. The system provides recommendation to visitors based on previously solved cases and new query given by the tourist. For this study, about 615 cases which are collected from National Tour operation and 10 attributes which are collected from experts are used as case base. These attributes and cases are used as knowledge base to construct case based recommender. The system calculates similarity between existing cases and new queries that are provided by the visitors, and provide solution or recommendation by taking best cases to the new query. In this study, JCOLIBRI case base development tool is used to develop the prototype. JCOLIBRI contains both user interface which enables visitors to enter their query and programming codes with the help of Java script language. To decide the applicability of the prototype in the domain area, the system has been evaluated by involving domain experts and visitors through visual interaction using the criteria of easiness to use, time efficiency, applicability in the domain area and providing correct recommendation. Based on prototype user acceptance testing, the average performance of the system is 80% and 82% by domain experts and visitors respectively. The performance of the system is also measured using the standard measure of relevance (IR system) recall, precision and accuracy measures, where the system registers 83% recall, 61% precision and 85.4% accuracy.
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Redesign Sanjai Chips Packaging Using Kansei Engineering Method
Scientific article
Sanjai chips made from cassava is typical tradisional Minang food. Packaging design is fundamental because 89% of consumer responses to buying a product are influenced by packaging. Current packaging weakness are plastic packaging is thin, no accompanying brands, and unattractive packaging, so less attractive, greasy, leaks, and make the chips contained in it be destroyed when bought for souvenirs. For this reason, it is necessary to redesign packaging for sanjai chips to increase product protection and influence consumers. Kansei Engineering is a method that deals with the psychological aspects of consumers when interacting with products to obtain a relationship between feelings and product characteristics. Because of that, this method has been chosen. The kansei process begins with a span of semantic space which begins with determining the kansei word. Kansei words were obtained from the distribution of open questionnaires and also based on previous research. After that, the span of the space of properties is determined which is useful for identifying what product properties should be in the design. after that the synthesis, design and evaluation are carried out. Based on the results from the results of the synthesis of span of semantic space and span of the space of properties, it was found that to design Sanjai Chips packaging, the criteria for packaging design are simple, good, informative, unique, and convincing for packaging materials must be non-greasy, made of plastic, good, strong, and convincing, while for other facilities criteria must be flexible.
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