Predictive Modeling of Global Educational Outcomes: A Comparative Analysis Using Machine Learning Regression Techniques

Abdelhameed; Abdelaziz A.; Ehab M.

doi:https://doi.org/10.54216/JAIM.070206

International Journal of BIM and Engineering Science | 2024

Integration of Project Management Professional (PMP) and Building Information Modeling (BIM) in the Construction Industry: Systematic Review

The construction industry is crucial for infrastructure and economic development. Integrating Building Information Modeling (BIM) and Project Management Professional (PMP) practices enhances performance, reduces costs and time, and improves planning and implementation, and boosts project quality and risk management. This research examines the benefits of BIM-PMP integration, such as improved project execution, cost and risk reduction, and enhanced team communication. It also addresses technical, cultural, and organizational challenges. Using statistical analysis with IBM SPSS and AMOS, the study investigates relationships between BIM, PMP, and variables like cost, time, and quality, based on expert interviews. Findings highlight BIM's role in achieving high quality, cost-effective, timely project completions and the need for a BIM-PMP framework to streamline operations and achieve project goals. Future directions include developing new tools, enhancing training, and supporting innovation to improve project performance.

groups

Manar Jreij mail -

Mohamad Shaban mail -

Nesrine Roumieh mail

link https://doi.org/10.54216/IJBES.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2024

Quality Management Procedures and Applications for Implementing Manufacturing Integration Modeling in an Engineering Company

Engineering companies in Syria suffer from a lack of comprehensive quality management implementation, leading to inaccuracies in components and design flaws during execution. This research aims to develop quality procedures for implementing Manufacturing Integration Modeling (FIM) in engineering firms. The study is of significant importance in enhancing efficiency, productivity, and fostering collaboration among different departments, while mitigating risks and ensuring safety. The research addresses challenges[1] such as difficulties in accessing information, participant availability, and data consistency, and poses questions on improving quality management procedures to tackle these challenges. Through literature review, case studies, and interviews with industry experts, the research seeks to provide recommendations to enhance the quality and efficiency of engineering operations in Syria post-crisis, thereby contributing to sector development and boosting innovation and competitiveness for engineering companies.

groups

Yara Kablan mail -

Rana Maya mail

link https://doi.org/10.54216/IJBES.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2024

The Role of Building Information Modeling in the Management and Operation of Existing Buildings

This research aims to highlight the importance of Building Information Modeling (BIM) in improving the management and operation of existing buildings. The study illustrates that achieving integrated functional performance for buildings requires the adoption of modern technologies, especially in the operational phase of their lifecycle. Despite BIM being primarily used in the design and construction phases, its application in operation and maintenance remains limited due to certain challenges, such as information management. The research presents a case study of Homs Museum as a model of a facility that relies on traditional methods for its operation, where BIM was applied to enhance three operational aspects: the container (building modeling and space occupancy), the content (indexing and classifying artifacts), and the user (staff management and BIM adoption within the institution). The results showed that BIM significantly enhances operational information management with high flexibility, underscoring the necessity of its adoption in existing buildings. The research recommends increasing the reliance on BIM in the operational phase, supporting its use in existing buildings through training and providing data collection tools, and developing user-friendly software interfaces for project managers.

groups

Heba Eid mail -

Alaa Kadi mail -

Hadi Kherbeic mail

link https://doi.org/10.54216/IJBES.080205

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

On the Two-Fold Maximal Units in Some Two-Fold Finite Neutrosophic Rings Modulo Integers for 2≤n≤5

In this paper, we have defined the concept of two-fold maximal units in finite two-fold neutrosophic rings modulo integers, where a sufficient and necessary condition for such class of generalized units will be provided. We characterize all maximal units in the following two-fold neutrosophic rings(Z_n (I))_(f_I ) for nE{2,3,4,5}.

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Isra Al-Shbeil mail -

Ibraheem Abu Falahah mail -

Talat Alkhouli mail -

Ahmed Soiman Rashed Alhawiti mail -

Jenan Shtayat mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.250213

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

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Journal of Artificial Intelligence and Metaheuristics | 2024

CO2 Emissions Forecasting Using Time Series Analysis and Metaheuristic Optimization for Environmental Sustainability

CO2 emission prediction is crucial for environmental policy and climate change mitigation. This review explores time series analysis and metaheuristic optimization in CO2 forecasting, summarizing research findings and methodological insights. Time series analysis uncovers past patterns and future trends, while metaheuristic methods like genetic algorithms optimize forecasting accuracy. Challenges include data quality, model complexity, and computational demands. However, the potential of advanced machine learning is a beacon of hope. It can revolutionize CO2 forecasting, making it more accurate and efficient. Composite models combining approaches show promise alongside real-time data integration and advanced machine learning. Future research should prioritize comprehensive databases and, importantly, stress the need for interdisciplinary collaboration to refine models. Improvements in forecasting can aid policy decisions and combat climate change, highlighting the growing need for accurate CO2 predictions and advanced analytical techniques.

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Ahmed El-Sayed Saqr mail -

El-Sayed M. El-Kenawy mail -

Mohamed S. Saraya mail

link https://doi.org/10.54216/JAIM.070203

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Advanced IoT Framework for Optimizing Sunflower Seed Production in Uzbekistan: Integration of Multi-Environmental Sensors

The current work focuses on the establishment of an enhanced Internet of Things (IoT) model in expectation to improve the sunflower seeds output in Uzbekistan. The presented framework involves examination of air quality, soil moisture, temperature, humidity, light intensity, GPS and weather station which is anticipated in giving a complete control and monitoring of the environmental probes at real time. The main goal is to establish an argument that such architecture will increase the yields in agriculture. It is done by simulating a model based on correlation and regression on secondary data which shows that the model will provide solutions to the problems associated with conventional farming which include conventional approaches towards provision of water and failure to internalize the conditions within which farming activities occur. The connection of the proposed sensors with the platform based on Arduino allows to gather and analyze the data that is essential for making appropriate decisions by the farmers. As the results the use of the developed framework in selected fields of sunflower will enhance yield with a potential of up to 25% in yield increase. Thus, the results shows that the implementation of such an innovative IoT architecture can greatly help farmers to increase efficiency, make proper use of resources, and minimize the negative effects on the environment while contributing to the development of sustainable agriculture. At the end the study recommends that further studies shall include more variables in the framework and test it for other crops and in other regions.

groups

Danish Ather mail -

Abu Bakar Bin Abdul Hamid mail -

Binti Ya’akub mail -

Rubina Liyakat Khan mail -

Pooja mail -

Rajneesh Kler mail

link https://doi.org/10.54216/JISIoT.140114

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Exploratory Data Analysis of International Student Demographics: Trends, Insights, and Implications

This paper focuses on Exploratory Data Analysis of the data from the “International Student Demographics,”which is available on Kaggle and comprises data collected through the academic years, as well as total students, U. S students, undergraduate, graduate, non-degree students, and OPT columns. In the given work, the author intends to provide a chronological overview of the demographic data of international students. Operations like handling missing values and outliers were done to prepare the data for a more elaborate analysis. All descriptive analyses during the study included time series plots and bar charts where time series was used to evidence key trends and distributions. The analyses of the research questions indicate that there has been growth in international student enrollment over the decades, particularly in undergraduate and OPT student categories, with influences from world events such as COVID-19 and the alteration of immigration policies. Country-wise contribution reveals that the maximum number of articles originated from East Asia and South and Central Asia, with a special focus on engineering, social sciences, and humanities. Solutions: The paper articulates the finality of trends affecting educational institutions and policymakers by focusing on the implications of international students’ demographics. Based on the findings above, future research directions are proposed to improve the findings and support evidence-based practice relating to international education.

groups

Sekar Kidambi Raju mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JAIM.070204

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

Proposed Two-Parameter Estimator for Estimating Linear Regression Model and Comparing It with Some Other Estimators

In this paper, a new two-parameter estimator was proposed to estimate the parameters of the linear regression model that has the ability to face the problem of Multicollinearity based on the previous information about the parameters to be estimated and this estimator was compared with the two-parameter estimator of the linear regression model of Kaciranlar and the two-parameter estimator of the linear regression model (Lokman et al. [1]) using the mean square error criterion (MSE) for each model by conducting Monte-Carlo simulation to study the behavior of the proposed estimator. It was concluded that the proposed method is better than the rest of the estimation methods because it achieved the lowest comparison criteria, and in the case of high Multicollinearity between the explanatory variables, the proposed method was very effective in solving this problem. Data representing (100) observations of the number of women with Irritable Bowel Syndrome (IBS) for the years (2020-2023) from the Karbala Holy Health Department were used, which represents the dependent variable (y) and a group of variables affecting the incidence of the disease, with nineteen variables. It was concluded that irritable bowel syndrome among women is decreasing, as the predictive values according to the proposed method are appropriate for the estimated values during the next five years.

groups

NoorAlzahraa Naeem Abd Ali mail -

Shrooq Abdul Redha Al Sabah mail

link https://doi.org/10.54216/PMTCS.040201

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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Journal of Artificial Intelligence and Metaheuristics | 2024

Predicting Student Outcomes: Evaluating Regression Techniques in Educational Data

Student performance prediction is essential so that institutions can assist in identifying weak performers and initiate corrective measures. This research assesses different regression models by applying data from Kaggle, which involves data cleaning like managing missing values and scaling of the data, hence feature extraction, then model imposition and authenticity. The models followed are Linear Regression, SVR, MLPRegressor, Gradient Boosting, Catboost, Xgboost, Random Forest, Extratrees, Decision Tree and K-neighbors. The analysis shows that Linear Regression produced the best result as it has the lowest MSE score of 0.000521 and high accuracy regarding other measures, including RMSE, MAE, and R². The results reveal that regression models can be used to predict students’ performance and be helpful to the various stakeholders in the system. The findings of this study will help develop required models for decision-making to improve students’performance.

groups

Manish Kumar Singla mail -

Faris H. Rizk mail -

Mahmoud Elshabrawy Mohamed mail -

Ahmed Mohamed Zaki mail

link https://doi.org/10.54216/JAIM.070205

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Predictive Modeling of Global Educational Outcomes: A Comparative Analysis Using Machine Learning Regression Techniques

Education contributes a crucial portion to the world’s development; thus, it is crucial to focus on education enrollment and quality education. It is essential not only that children enroll in school but also that they receive proper education to improve individuals and, consequently, society. This paper aims to use machine learning to predict educational outcomes based on the World Educational Data obtained from Kaggle to analyze the data, preprocess it, and evaluate the performances of the different regression models. The following models consist of Support Vector Regression (SVR), CatBoost, RandomForestRegressor, ExtraTreesRegressor, GBoost, MLPRegressor, GradientBoosting Regressor, DecisionTreeRegressor, KNeighborsRegressor, LinearRegression, and Pipeline. Evaluation measures used included MSE, RMSE, MAE, MBE, r, R2, NSE, and WI. Analyzing the performance comparison, the best accuracy was associated with CatBoost with an r value equal to 0.999996 and an R2 value of 0. 999993; The MSE score was 0.04024. The outcomes of the present paper demonstrate that the application of advanced machine learning algorithms can be used effectively to predict educational outcomes, thus enabling policymakers and educational planners to use them for designing effective educational policies and overcoming existing global challenges in the sphere of education.

groups

Abdelhameed Ibrahim mail -

Abdelaziz A. Abdelhamid mail -

Ehab M. Almetwally mail

link https://doi.org/10.54216/JAIM.070206

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new