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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: 680
Статья научная
This paper analyzes the performance of hybrid gigabit passive optical network (GPON) and wireless communication (FSO) system using EDFA and EYDWA as pre-amplification configuration to compensate the losses due to the fiber cable and the free space channel for 2.5 Gb/s of data. The performance for both amplifiers has been compared on the basis of distance, FSO range and number of users, however the results of simulation show enhancement offered by EYDWA as compared as EDFA amplifier. This amplifier was able to reach transmission distance over 245 Km optical fiber and 1Km range FSO with best BER value around 10-9 and good eye diagram. Whereas, the fiber distance has been limited to 64 Km by using EDFA amplifier.
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Phylogenetic Method for High-Throughput Ortholog Detection
Статья научная
Accurate detection of orthologous proteins is a key aspect of comparative genomics. Orthologs in different species can be used to predict the function of uncontrived genes from model organisms as they retain the same biological function through the path of evolution. Orthologs can be inferred using phylogenetic, pair-wise similarity or synteny based methods. The study here describes a computational method for detecting orthologs of a protein. A phylogenetic tree based approach is used for identification of orthologous proteins. A Combination of species overlap algorithm and patristic distances is used for detecting orthologs of a protein from a set of FASTA sequences. Patristic distances have been used to drill the orthology predictions of any protein down to its closest orthologs. The approach gives a considerably good accuracy and has high specificity and precision. The use of Distance threshold allows controlling the stringency level of predictions so that the closeness and proximity between the protein of interest and its orthologs can be adjusted.
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Platform Screen Doors Enhanced Bus Rapid Transit Intelligent Performance
Статья научная
It is the last straw to clutch at to solve the urban traffic issue, to develop high-capacity rapid transit and promote transit priority. Characterized by low cost, short construction cycle and flexible development, Bus Rapid Transit (BRT) has been favored by more and more cities in the world. The Platform Screen Doors (PSDs) system is an important component at BRT stations, and it is original motive to be designed to satisfy the growing demand from BRT application to provide increased safety and comfort in the first. With the continual increased demand for BRT intelligent performance, the PSDs system is applied to get Bus Location Information with accurate position of arrival and departure at stop, to provide Real-Time Information (RTI) for BRT passengers using PSDs/GPS compound location technology, to put into practice Bus Fleet Management (BFM). Considering the capability of accurate location, it can be applied to actualize Bus Sign Priority in the future. The authors are luck to take in part the practice of BRT system, especially in the BRT intelligent systems, the papers will introduce upwards application and conceive in detail.
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Статья научная
Tiltmeters with high accuracy and sensitivity are indispensable for various geotechnical applications, including soil deformation monitoring, structural inclination analysis, and seismic activity assessment. This study proposes a novel tiltmeter system utilizing Parallel Dipole Line (PDL) technology, where a diamagnetic graphite cylinder is levitated within a camelback potential field generated by parallel magnetic dipoles. Variations in the vertical position of the graphite cylinder correspond to tilt angles, which are captured by a high-resolution imaging system and processed using a Jetson Nano microcomputer for real-time analysis. Experimental results show that shorter graphite lengths can increase the measurement range. One of the test results is that 6 mm graphite can measure inclination in the range of -1.00000° to +0.99999°. In contrast, longer graphite, such as 12 mm, only reaches a range of -0.60000° to +0.60434°. In addition, the increase in graphite length and the reduction in magnet dimensions significantly help reduce oscillations during measurement, which ultimately improves system stability. The optimized PDL-based tiltmeter is capable of detecting inclination with a high resolution of up to 10⁻⁵ degrees, with critical damping used to eliminate oscillatory interference. These findings confirm that the PDL tiltmeter system offers much better precision, stability, and durability than conventional methods, making it a potential innovative tool for high-resolution geotechnical and structural monitoring.
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Power Aware Reliable Virtual Machine Coordinator Election Algorithm in Service Oriented Systems
Статья научная
Service oriented systems such as cloud computing are emerging widely even in people’s daily life due to its magnificent advantages for enterprise and clients. However these computing paradigms are challenged in many aspects such as power usage, availability, reliability and especially security. Hence a central controller existence is crucial in order to coordinate Virtual Machines (VM) placed on physical resources. In this paper an algorithm is proposed to elect this controller among various VM which is able to tolerate multiple numbers of faults in the system and reduce power usage as well. Moreover the algorithm exchanges dramatically fewer messages than other relevant proposed algorithms.
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Статья научная
A novel lumped-parameter model is proposed to help with establish practice criteria of decompressive craniectomy and explain post-craniectomy intracranial dynamics. Besides traditional four major parts of arterial and venous blood, cerebrospinal fluid and brain tissue, another compartment produced by secondary intracranial hypertension is included here. The elliptical deflection solution under uniformly distributed pressure is introduced to compute the craniectomy compartment volume and incorporate it into existing differential equations. Under particular pathology in this paper our model predicts the waveform of post-craniectomy intracranial pressure, which measures the clinical effectiveness of such an operation. Then a statistical model—Gaussian fitting model is used to fit our simulation data. This quantitative model provides a possible way to designate the operation criteria such as the size of decompressive craniectomy. Finally we propose the optimal interval of craniectomy size as from 100 to 300 square centimeters and that larger than 400 square centimeters would not obviously reinforce pressure reduction anymore.
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Predicting Shelf Life of Burfi through Soft Computing
Статья научная
Soft computing cascade multilayer models were developed for predicting the shelf life of burfi stored at 30oC. The experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output variable. The modelling results showed excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.993499439) and low RMSE (0.006500561), indicating that the developed model was able to analyze nonlinear multivariate data with very good performance, and can be used for predicting the shelf life of burfi.
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Статья научная
A modern technology used for extracting knowledge from a huge amount of data using different models and tasks such as prediction and description is called data mining. The data mining approach has a great contribution on solving a different problem for data miners. This paper focuses on the application of data mining in health centers using different models. The model development process helps to identify or predict the behavior of blood donors whether they are eligible or ineligible to donate blood by their right status way and protects any blood bank health center from the collection of unsafe blood. Classification techniques are used for the analysis of Blood bank datasets in this study. For continuous blood donors, it will help to enable to donate voluntary individuals and organizations systematically. J48 decision tree, neural network as well as naïve Bays algorithms have been implemented in Weka to analyze the dataset of blood donors. The study is used to classify the blood donor's eligibility or ineligibility status based on their genders, deferral time, weight, age, body priced, tattoos, HIV AIDS, blood pressure, donation frequency, hepatitis, illegal drug use attributes. From the 11 attributes, gender does not affect the result. We have used 1502 datasets for the train set and 100 datasets for testing the model using cross-fold validation. Cross-fold data, partition was used in this study. The efficiency and effectiveness of the algorisms are measured automatically by the system. The obtained result showed that the J48 classifier outperforms the best result as well as both neural network and navies, Bayes, in terms of matrix evolution, with its 97.5% overall model accuracy has offered interesting rules.
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Статья научная
Many study programs at universities face issues, including students experiencing delays in graduation, which hinders the completion of their studies on time. These delays in student graduation contribute to a decrease in the accreditation score of the Information Systems program. One solution to address this issue is to develop a data-mining-based system to monitor and utilize student progress data by predicting their graduation status using the C4.5 Decision Tree algorithm. This research process involves several stages: problem analysis, data and system design, coding, testing, and finally, maintenance. The outcome of this research is the implementation of the C4.5 algorithm to predict students' timely and delayed graduation. The data used includes records of students who graduated in 2021 and 2022. The acceptance rate, calculated using a confusion matrix, demonstrates an accuracy level of 92.16%, based on a dataset of 119 training data points and 51 testing data points, or 70% training to 30% testing ratio. The results of this research and testing indicate that the C4.5 Decision Tree algorithm is highly suitable for predicting student graduation outcomes.
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Preparing Mammograms for Classification Task: Processing and Analysis of Mammograms
Статья научная
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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Статья научная
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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Статья научная
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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Problems of Regulation and Prospective Development of E-commerce Systems in the Post-coronavirus Era
Статья научная
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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Статья научная
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
Статья научная
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
Статья научная
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
Статья научная
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
Статья научная
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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Статья научная
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
Статья научная
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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