The Impact of Building Material Modeling on Enhancing Building Sustainability (Energy Efficiency): A Case Study of a Residential Building in Basilia

Mary Abou; Naoras; Alaa J

doi:https://doi.org/10.54216/IJBES.100204

Metaheuristic Optimization Review | 2025

Metaheuristic Algorithms in Optimizing Structural Design of Bridges: A Review

Metaheuristic optimization algorithms become essential to solving structural design problems because they can handle nonlinear, multiple-mode, large-scale, and other difficulties. This review focuses on how MOAs have been developed and utilized and how they have compared efficiency in structural engineering design optimization. It describes some of the main milestones, such as hybrid and ensemble algorithms, as well as quantum annealing and finite elements, to improve the accuracy of the results. The study organizes and assesses modern approaches scientifically and accentuates their benefits and pitfalls in practical applications. Hypotheses derived from benchmarking and statistical exercises show that enhanced MOAs are reliable and fast in yielding almost ideal structures within a manageable computational frontier. Finally, the review outlines the limitations of the current research and suggests research foci for the future advancement of metaheuristic methods and their use in structural engineering optimization.

groups

Sekar Kidambi Raju mail

link https://doi.org/10.54216/MOR.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2025

A Review of Machine Learning Models for Predicting Air Quality in Urban Areas

Air pollution is a critical environmental issue that threatens almost the world, and public health, ecosystems, and the sustainability of cities are affected by the severe impacts of air pollution. Urbanization and industrialization have been on the run, with escalating pollution levels. Hence, air monitoring and air quality prediction are necessary for such challenges. This review discusses advanced machine learning (ML), deep learning (DL) techniques, and IoT-based study hybrid frameworks for air-quality prediction in urban settings. Integration of different data sets such as meteorological parameters, concentrations of pollutants, and data from satellite imagery, these technologies provide strong and scalable solutions for real-time monitoring and forecasting. Some of the advancements include the use of IoT-enabled sensors, the use of convolutional and recurrent neural networks, and the development of location-specific predictive models. Despite significant evolution, several challenges of data sparsity, computational requirements, and model adaptability remain. This paper casts the technologies as transforming cities into smart and green cities and advancing the cause for continuous innovation and interdisciplinary collaboration to strengthen their effectiveness. These findings add to the advancement of knowledge on air quality prediction methodologies and their crucial role in sustainable urban development.

groups

El-Sayed M. El-kenawy mail

link https://doi.org/10.54216/MOR.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2025

Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications

Forecasting carbon dioxide emissions from vehicles is essential for sustainable transportation, climate-aware urban planning, and intelligent environmental management, as road transport contributes substantially to energy-related greenhouse gas emissions. Rapid growth in vehicle ownership, mobility demand, fuel use, freight activity, and congestion has exposed the limits of conventional emission-estimation methods, particularly when nonlinear and multi-factor relationships must be represented. This review examines how artificial intelligence and machine learning support vehicle carbon dioxide emission forecasting through data-driven models, deep learning, feature selection, hyperparameter optimization, and metaheuristic algorithms. It emphasizes the value of integrating traffic volume, vehicle attributes, fuel type, speed patterns, road conditions, temporal behavior, weather variables, and energy-consumption indicators to improve prediction reliability under real-world conditions. Optimized forecasting models can support emission monitoring, mitigation planning, traffic management, and low-carbon mobility policies, although challenges remain in data availability, interpretability, uncertainty, regional transferability, computational cost, and transparent validation.

groups

Mahmoud Elshabrawy Mohamed mail

link https://doi.org/10.54216/MOR.030205

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2024

Characteristics Neutrosophic Ideals For Neutrosophic Rings: On Review

The main objective of this paper is to present a review study with more information on the neutrosophic Ideal, Principle Ideal, Prim Ideal, Pseudo Neutrosophic Ideal, Quotient ring, and Pseudo Quotient ring. Neutrosophic ring theory is a branch of neutrosophic Algebra which introduced by Florentin Smarandache in 2006.

groups

Shawqi Al-lkami mail -

Adel Al-odhari mail

link https://doi.org/10.54216/PAMDA.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

Fuzzy Generalized Poisson Doubles Hurdle Model (FGPDH) on the Leukemia in Iraq

and Fuzzy Generalized Double Hurdle (FGPDH)—to estimate and predict patient outcomes. We used the Firefly Algorithm to optimize and estimate the parameters for these models. Among them, the FGPDH model consistently provided the most accurate predictions, closely matching the actual values. The Generalized Double Hurdle model also performed well, significantly improving accuracy by capturing the complexity of the data. In contrast, models like Poisson, Single Hurdle, and Double Hurdle Poisson showed less predictive accuracy due to higher error rates. Our proposed FGPDH model, enhanced with the Firefly Algorithm, effectively handles uncertainty and complexity, making it the most reliable and precise approach in this context.

groups

Adel Abbood Najm mail -

Bashar Khalid Ali mail

link https://doi.org/10.54216/PMTCS.040205

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Intelligent Remote Sensing Scene Classification Model for On-Board Training of Resource-Constrained Devices

Remote Sensing Scene Classification (RSSC) is the distinctive classification of remote sensing images into numerous classes of scene classifications based on the image content. RSSC plays a significant role in several domains, like land mapping, agriculture, and the classification of disaster-prone regions. The Internet of Things (IoT) is a dynamic global network of devices, for example, vehicles, sensors, actuators, surveillance cameras, etc. These interconnected objects were distinctively recognizable and they could separately transfer and obtain valuable data through the network. However, satellite images were frequently degraded and blurred owing to aerosol dispersion under haze, fog, and other weather circumstances, decreasing the color fidelity and contrast of the image. To use effectual RSSC in real-time, widespread researchers concentrate on creating aerospace image processing systems, like airborne or spaceborne systems. Recently, with the quick improvement of deep learning (DL) and Machine learning (ML) techniques, the performance of RSSC has significantly developed owing to the hierarchical feature representation learning. Both technique has greater achievement in the domain of image scene classification. This study presents a Leveraging Tiny Convolutional Neural Networks with a Water Cycle Algorithm for Remote Sensing Scene Classification (LTCNN-WCRSSC) model. The LTCNN-WCRSSC technique is designed for efficient RSS classification in resource-constrained devices with on-board training capabilities. At first, the LTCNN-WCRSSC model applies image processing using a median filter (MF) to eliminate the noise. Next, the feature extraction process can be exploited by the ConvNeXt-Tiny method. For the RSSC model, the spatiotemporal attention bidirectional long short-term memory (STA-BiLSTM) technique is performed. Eventually, the water cycle algorithm (WCA)-based hyperparameter choice process can be performed to optimize the classification results of the STA-BiLSTM algorithm. The experimental evaluation of the LTCNN-WCRSSC technique takes place using a benchmark image dataset. The stimulated results indicated the superior performances of the LTCNN-WCRSSC model over other approaches.

groups

Ahmad Khaldi mail -

Josef Al Jumayel mail

link https://doi.org/10.54216/JCHCI.090101

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Proposal for a BIM Adoption Framework in the Syrian Engineers Syndicate: A Case Study of the Homs Branch

The application of the Building Information Modelling (BIM) concept has become an indispensable necessity in the Architecture, Engineering, Construction, and Operations (AECO) sector. This has been evident in the experiences of numerous Arab and foreign countries that have changed their policies and issued new standards, guidelines, and codes for implementation. In Syria, we must also embark on the reconstruction phase, with its massive investment projects, using BIM technology. The primary driver of this change will undoubtedly be the government sector by imposing new policies at all levels across all institutions. Therefore, this study aims to highlight the mechanism of the Syrian Engineers Syndicate as a political authority capable of making structural modifications to the policies followed in carrying out engineering works and, from its position, able to mandate the use of BIM. Accordingly, the researcher analyzed the internal system of the Syrian Engineers Syndicate to examine the required modifications and proposed a framework for adopting BIM in engineering syndicates. The study focuses on establishing a "Building Information Modelling and Management Committee" within the Engineers Syndicate in Homs Governorate as a case study, suggesting its structure, job titles for its members, and their roles. This study aims to develop current policies and create the first-of-its-kind guide for engineering syndicates in Syria. The researcher relied on the content analysis method of previous studies to benefit from international experiences related to the importance of activating the government’s role in adopting the BIM concept. Additionally, the researcher adopted the strategic plan methodology for the adoption of BIM in Syria, considering it the general guide and leader in the digital transformation process in Syria

groups

Nour Kabbani mail -

Sonia Ahmed mail -

Raghad Safour mail

link https://doi.org/10.54216/IJBES.100201

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Financial Technology and Innovation | 2025

Remedy-Aware Financial Innovation: A Hierarchical RegTech Framework for Consumer Complaint Resolution

Financial complaint systems keep track of instances of service failure as well as of the remedy chosen by the responding institution. The crucial question for operational supervision isn’t just one of categorization of complaints, however; compensation is scarce, expensive, and fits into a larger framework of explanation vs. relief. In this paper, an approach for the development of a regulatory technology framework for remedies is proposed, which conceptualises the resolution of complaints as a hierarchy. The first stage determines if a case will be decided on relief or explanation, and the second stage distinguishes monetary from non-monetary relief. This is done by using sparse and regularized models, which are calibrated on the following validation period, and tested on an untouched chronological holdout, combining structured intake attributes with complaint narratives. In the empirical application, 268,570 complaints were received by California in 2024. Monetary relief makes up 1.04% of independent test period, and accuracy is not the best criterion. The hierarchical model yields a monetary-relief precision-recall area of 0.335, whereas the flat text-tabular model and a flat model with the event prevalence yield 0.314 and 0.010, respectively. At the 2 percent review budget, it is able to identify 58.9 percent of monetary-relief cases and has a lift of 29.4 times over a random review. The flat fusion model is slightly better for overall three-class classification, demonstrating the benefit of a hierarchy of remedies when institutional capacity is focused on rare, consequential outcomes, rather than labelling. Results provide a practical design of human-supervised complaint triage while retaining calibration, interpretability, chronological validation, and clear usage limitations for automated complaint analysis.

groups

Laith Farhan mail -

Raad S. Alhumaima mail

link https://doi.org/10.54216/FinTech-I.050201

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Integrating Building Information Modeling (BIM) into Architectural Education: Pedagogical Challenges and Future Prospects: Case Study: Tartus University

This study investigates the challenges hindering the integration of Building Information Modeling (BIM) into architectural curricula in Syria, particularly at Tartus University. Despite growing industry recognition of BIM's benefits, academic institutions have exhibited initial resistance. The study analyzes existing BIM curricula, compares them to global benchmarks, and identifies key obstacles such as weak industry-academia links, insufficient resources, traditional teaching methods, and a lack of BIM expertise among faculty. To address these challenges, the study proposes a framework that includes strengthening industry-academia partnerships, enhancing financial and technical support, updating curricula and pedagogy, investing in faculty development, and establishing BIM centers of excellence. By implementing these strategies, Syrian universities can effectively integrate BIM into their curricula, bridging the gap between academia and the professional architectural community.

groups

Zeina Naddeh mail -

Rana Maya mail -

Waleed Mahfouz M. A. Youssef mail

link https://doi.org/10.54216/IJBES.100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2025

The Impact of Building Material Modeling on Enhancing Building Sustainability (Energy Efficiency): A Case Study of a Residential Building in Basilia

This research aims to study the impact of building materials modeling on enhancing building sustainability through a case study of a residential building within the Basilia City planning scheme in Damascus, which is being transformed into a sustainable area using sustainable design techniques. The study is based on analyzing plot EA-189 in the Kfar Sousseh area by evaluating the thermal efficiency of the proposed building materials and comparing them with other materials using the Insight Solar tool in Autodesk Revit.The study involves analyzing three scenarios using different building materials: (1) Concrete blocks with air insulation and a bituminous insulated roof, (2) Brick with fiberglass insulation and a green roof, (3) A mix of brick and concrete blocks on different facades and a green roof. The results show that the second scenario provides the highest energy efficiency and best indoor air quality, despite its higher cost. The third scenario, which combines brick and concrete blocks, offers a balance between energy efficiency and cost, making it the optimal choice for future construction projects. The study demonstrates that building information modeling (BIM) enhances the effectiveness of sustainable design strategies and contributes to improved energy performance and environmental comfort of buildings.

groups

Mary Abou Sekka mail -

Naoras Khalil mail -

Alaa J Kadi mail

link https://doi.org/10.54216/IJBES.100204

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new