- All articles 732
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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: 732
Phylogenetic Method for High-Throughput Ortholog Detection
Scientific article
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
Scientific article
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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Scientific article
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
Scientific article
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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Predicting Diabetes Using Machine Learning: Models and Insights
Scientific article
Diabetes mellitus is a chronic metabolic disorder with a rapidly increasing global prevalence, posing a significant public health challenge. Early detection of diabetes can enable timely intervention and preventive measures, thereby reducing the risk of long-term complications. In this study, a machine learning (ML)-based methodology is proposed for the early prediction of diabetes mellitus. The proposed approach enhances existing prediction systems by improving key performance metrics, including precision, recall, and F1-score, and achieves an efficiency improvement of 4%–10% compared to state-of-the-art methods. Experimental results demonstrate that the support vector machine outperforms other ML algorithms for diabetes prediction, achieving 92% accuracy, 95% precision, 92% recall, 93% F1-score, 92% specificity, and an area under the receiver operating characteristic curve of 0.97.
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Predicting Employee Attrition: A Machine Learning Approach in Human Resource Analytics
Scientific article
Employee attrition is an important factor that can affect organizations, both financially and operationally. Human Resource (HR) managers often find it difficult to identify exactly which employees might be planning to leave the organization and what is the root cause for their decision. With the recent advances in computing, Machine Learning (ML) techniques are available for analysing, understanding, and solving complex problems. This study analyses the IBM HR Analytics dataset using ML techniques to predict employee attrition and identify the key factors that influence attrition. Four ML models based on Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting have been used for analysing attrition. It is found that Logistic Regression outperformed all other models in predicting attrition. At the same time, Decision Tree is found to be the weakest among the four techniques. On the analysis of feature importance, it is found that variables related to compensation (Monthly Income), career stage (Total Working Years, Age), and tenure at the organization are among the most significant factors influencing attrition. The insight from this study is expected to help HR managers in developing effective, data-driven strategies to retain their talent in their organization.
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Scientific article
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
Scientific article
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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Scientific article
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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Scientific article
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
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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Scientific article
Proactive prediction based channel allocation for Cognitive Radio Networks (CRNs) remains a challenging area of research. In Internet of Things (IoT) scenarios, prediction algorithms must balance resource efficiency with high accuracy. The proposed algorithm, ProCLAMUS, a Matrix Completion (MC) Based Proactive Spectrum Allocation Protocol for CRNs, couples nuclear norm matrix completion with short horizon sliding window prediction. By reconstructing sparse spectrum sensing data and down weighting inconsistent reports, ProCLAMUS enables infrequent sensing and decentralized decisions without a fusion center, reducing energy and attack surface. In a network of 40 Secondary Users (SUs), 400 Primary Users (PUs) and 400 channels with malicious data ranging from 5 to 50%, ProCLAMUS sustains the highest channel utilization (avg 89.96%) with the lowest backoff rate (3.81 s^(-1)) and sensing delay (0.58 channels/success). ProCLAMUS achieves the lowest radio energy (16.82×10^(-3) J/s), a 44–53% reduction when compared with recently proposed techniques, while using 33–43% less memory. These gains arise from sparse sensing, fusion (majority voting) and conservative allocation. The results demonstrate superior energy efficiency and robust prediction under malicious data conditions during the Spectrum Sensing Data Falsification Attack.
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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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Scientific article
This study develops a Python-based hybrid forecasting framework that integrates classical Decline Curve Analysis (DCA) with Support Vector Regression (SVR) to improve the reliability of oil production forecasting and the estimation of Estimated Remaining Reserves (ERR), which are subsequently used to derive cumulative CO₂ emission potential. Historical production data are segmented to represent boundary-dominated flow conditions, enhancing the stability and physical consistency of decline parameters. SVR is then applied to correct localized deviations in the historical response that are not captured by conventional DCA formulations, yielding improved decline representations for forecasting. The hybrid DCA–SVR model achieves a more coherent historical match and reduced average residual errors compared to standalone exponential, hyperbolic, and harmonic decline models. Using the hybrid framework, ERR estimates of 4.92×10⁵, 1.74×10⁶, and 3.62×10⁶ STB were obtained for the exponential, hyperbolic, and harmonic decline scenarios, respectively, corresponding to cumulative CO₂ emission potentials of approximately 213,036, 753,420, and 1,567,460 tons of CO₂. These scenario-based results provide a bounded range of emission outcomes, explicitly demonstrating that uncertainty in decline model selection propagates directly into long-term ERR and CO₂ emission estimates. Overall, the proposed hybrid machine learning–decline curve analysis (ML-DCA) framework offers a transparent and reproducible approach for linking production forecasting with quantitative CO₂ impact assessment in data-driven reservoir management.
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