Journal of Advanced Research Design

Adaptive Quantum Behaved Flower Pollination and Tabu Search Algorithm for Energy Efficient Task Management in Edge-Cloud Continuum

Nasiru Muhammad Dankolo — 2025-08-18

Efficient task management in a dynamic and resource-constrained edge-cloud environments is essential for minimizing execution delays and reducing energy consumption. This paper presents a novel hybrid optimization algorithm, integrating an Adaptive Flower Pollination Algorithm (FPA) with Tabu Search, to address these challenges. The proposed approach introduces a diversity-based adaptive mechanism for global and local search and leverages Tabu Search to refine solutions and prevent convergence to suboptimal points. Extensive simulations demonstrate that the proposed algorithm outperforms state-of-the-art models in reducing delays and energy usage while balancing resource utilization in both edge and cloud environments. These results highlight the significant improvements achieved over baseline algorithms, providing an effective solution for task scheduling in edge-cloud systems.

Chaotic Binary Manta-Ray Foraging Optimization Algorithm Based Descriptor Selection for Amphetamine-Type Stimulants Drug Classification

Nor Fatin Nabila Aziz — 2025-06-26

This study proposes a novel approach to optimize descriptor selection for classifying Amphetamine-Type Stimulants (ATS) and non-ATS drugs. The approach applies a Manta Ray Foraging Optimization (MRFO) framework enhanced with time-varying transfer function and a chaotic map. These enhancements aim to achieve a balance between exploration (finding new possibilities) and exploitation (refining promising solutions) within the improved version of binary MRFO (BMRFO) algorithm to solve descriptor selection problem. Two types of time-varying transfer functions recommended by earlier research are used to convert continuous MRFO to binary MRFO (BMRFO) for binary optimization problem. In addition, the pseudorandom number of the MRFO's probability operator is replaced with eleven different chaotic maps to prevent convergence problem. To evaluate the effectiveness of the suggested approach, a particular high-dimensional chemical dataset containing molecular descriptors of ATS and non-ATS drugs was utilized. The experimental results and statistical analysis show that the proposed BMRFOTV2-C9 employing the non-linear time-varying Sigmoid transfer function and Sinusoidal chaotic map is superior with lowest average fitness indicating its ability to convergence to optimal solution by selecting relevant descriptor subset and achieved better classification accuracy. Additionally, this study evaluated the performance of BMRFOTV2-C9 to other known BMRFO variations from the literature and the findings show that it is more efficient than others.

Exploring Compression-Only Structures and Innovations in Earthen Floor Slab Systems

Mustafa Safaa Hasan — 2025-06-26

This study presents a systematic literature review tracing the evolution of compression-only, earthen-based floor slab systems from early empirical design to modern computational methods. Earthen construction, renowned for its sustainability and natural aesthetic, historically relied on rule-of-thumb approaches for structural stability. Our review documents the transition from ancient masonry techniques and proportional rules to the adoption of static analysis and computational simulations, such as thrust line and force network analyses. From an initial pool of 300 sources, 99 were selected based on rigorous inclusion criteria. Data visualization with VOSviewer—via timeline density, cross-citation, and keyword maps revealed a surge in research interest since 2020 and underscored the lasting influence of foundational studies. Key themes include computational innovations, material trade-offs, and recent integration of materials science with structural analysis.

Multi-user Interaction in Collaborative Virtual Multi-class for Metaverse

Fendi Aji Purnomo — 2025-06-26

Multi-user Virtual Reality (VR) technology that supports multi-class has effectively addressed challenges in traditional face-to-face learning, including constraints related to location, time, and pandemics. This innovation is anticipated to deliver substantial cost savings across fields such as education, medical care, training, and industry. This study introduces a Unity-based multi-user collaborative virtual classroom model, themed around an anatomy laboratory, enabling users to interact within a shared virtual environment while supporting the creation of additional classrooms for diverse educational purposes. The system architecture consists of three layers: the application layer, which facilitates user interaction through 3D avatars and collaborative tasks; the framework layer, supporting multi-user communication and 3D anatomical model manipulation; and the engine layer, leveraging Unity and Photon Engine for multi-user functionality and device integration. The prototype was evaluated using the Technology Acceptance Model (TAM) with 30 first-time users, demonstrating strong reliability and validity across key dimensions: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATT), and Behavioral Intention to Use (BI). PEOU scored the highest (4.46 ± 0.56), indicating ease of use, while BI scored slightly lower (4.21 ± 0.60), suggesting opportunities for improving engagement. These findings highlight the potential of the Anatomart VR application to enhance anatomy education through an innovative, immersive, and collaborative learning environment.

Performance Evaluation of Machine Learning Models for Emotion Recognition

Naveen Palanichamy — 2025-06-26

Emotion recognition is a part of artificial intelligence that detects human emotions using machine learning (ML) techniques, thus analysing inputs like facial expressions, voice tone and physiological signals. Individuals with Autism Spectrum Disorder (ASD) experience difficulties in social interaction and communication and exhibit repetitive behaviours. ML has greatly helped autistic people by analysing their behavioural data to detect and comprehend slight emotional indicators. This paper explores the abilities of ML-based systems to recognize and understand these emotional cues, which are of great importance for improving communication and intervention methods. We employed datasets of EEG signals and applied principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) for dimension reduction. We used ML models such as Random Forest (RF), Support Vector Machines (SVM), Logistic Regression, Long Short-Term Memory (LSTM) and 1D Convolutional Neural Network (1D-CNN) on the datasets. The evaluation of the models relied on metrics like accuracy, precision, recall and F1-score to determine their ability to recognize emotions in EEG signals. The RF model achieved 95% accuracy on the original dataset and maintained robustness with PCA (88%) and UMAP (85%), outperforming other models in classification accuracy and stability. Future research should concentrate on broadening the datasets to include a more varied group of participants and combining multimodal data, using advanced deep learning methods to increase the accuracy and the feasibility of emotion recognition systems for personalized ASD therapies.

Integrated Vertical Aquaponics Monitoring System using Hibiscus Sense with Favoriot Platform

Abdul Ismail Abdul Rani — 2025-06-26

Aquaponics combines aquaculture and hydroponics, where fish and aquatic animals are grown in conjunction with soil-less plant cultivation in a circulated environment. Vertical aquaponics typically utilizes the nutrient film technique (NFT) method, employing pipes to continuously stream water to each channel, providing nutrients, water and oxygen to the plants. This project aims to develop a real-time monitoring system for the vertical aquaponics setup, using Hibiscus Sense along with Arduino Mega to measure pH, total dissolved solids (TDS) and water temperature to establish the optimal conditions for Bak Choy growth. All data is uploaded to the Favoriot IoT platform, which includes a dashboard to visualize the information. Results demonstrate a positive correlation between TDS and plant growth, indicating that plants efficiently utilize water nutrients for their development. The linear line values (R) for TDS and growth rate are close to 1.0, with respective values of 0.9234 and 0.9384, underscoring a strong relationship between TDS and plant growth. The ideal conditions for optimal Bak Choy growth are a high TDS value, ranging from 220 to 270 ppm and a pH value of 7.5. Additionally, maintaining a cool water temperature between 26°C and 29°C prevents plant shrinkage due to hot weather. The findings clearly indicate that TDS has a pronounced influence on the growth of Bak Choy, while temperature and pH values exhibit a relatively minor impact.

A Review of Current Cell Annotation Systems for Histopathology Images

Ling Teoh Chai — 2025-06-26

Histopathology techniques offer a unique way to study the structural and functional characteristics of biological model systems such as cultured cells, tissues and organoids. As the field of histopathology advances and more complex properties of living organisms are revealed through novel assays, there is a growing need for image analysis methods that are robust and easy to use. In many histopathology image analysis workflows, the first step is to segment cell nuclei, as they serve as the fundamental block for identifying individual cells in histopathology images. Such methods are crucial in a range of research studies, from counting cells and tracking moving populations to localizing proteins, classifying phenotypes and profiling treatments. Here, this review intended to provide an updated cell annotation system for histopathology images using the predetermined search strings and a set of inclusion criteria. Accordingly, 11 cell annotation systems (i.e., Image J, ASAP, Ilastik, Quanti.us, JS Segment Annotator, Labelme annotation tool, Labelbox, LabelImgTool, OpenSurfaces Segmentation UI, Cell Profiler and MATLAB Image Segmenter) was included. An in-depth discussion on the background of each included system was provided in this review alongside a brief comparison across different cell annotation systems.

Building Information Modelling in Hospital Facilities Management Services: A Significant Systematic Review

Azliya Ismail — 2025-06-26

Utilizing Building Information Modelling (BIM) in the context of hospital facilities has gained substantial attention due to its potential to revolutionize maintenance, operations, as well as decision-making processes. The systematic literature review investigates the effects, challenges and advantages associated with BIM application in hospital facilities management (FM), serving as a comprehensive resource for healthcare and construction industry professionals. However, a comprehensive comprehension with respect to the present state of study, the methodologies employed and the practical implications of BIM adoption in the context of hospital FM is lacking. The PRISMA framework serves as a guiding structure for systematic reviews and meta-analyses, ensuring the meticulous selection and analysis of pertinent studies while emphasizing transparency and methodological rigor throughout the process. Advanced searching techniques are employed to enhance the precision and inclusiveness of the literature search. The review specifically leverages the Scopus and Web of Science (WoS) databases, tapping into their extensive repositories of scholarly work. The synthesis of the literature yields a rich tapestry of insights, with expert validation emerging as a central theme. The results are organized into three key themes: (1) Advantages of BIM Implementation in Construction and Healthcare; (2) Effects of BIM on Construction Practices and Health Facilities; and (3) Issues and Challenges in BIM Adoption in Healthcare and Construction. The final finding data is (n=25), which review identified key themes. Results showcase the benefits of BIM implementation in optimizing maintenance practices and enhancing operational efficiency in healthcare settings. Finally, BIM can improve hospital facilities management by increasing maintenance, operational efficiency and decision-making. Despite issues with complexity and coordination, BIM's benefits make it important in healthcare. The literature review provides professionals with a clear path to efficiently implementing BIM in their work.

Modelling the Relationship of Building Information Modelling Implementation Barriers and Drivers in Sabah Construction Industry

Velerine Mei Shan Vun — 2025-06-26

Despite Sabah’s reputation as a rapidly developing state, the number of Building Information Modelling (BIM) performance is significantly lower than the adoption in Peninsular Malaysia. In fact, there was a lack of research explored on BIM specifically in Sabah. Therefore, this research is comprehensively aimed to understand and analyse the dynamics of BIM implementation in the construction industry in Sabah. The objective of the study was to determine the association of barriers that exists in Sabah’s construction industry that prevent BIM from being implemented there. The next stage involved conducting an analysis of the association of drivers that can drive Sabah’s construction industry toward greater adoption in BIM. This study utilized a quantitative approach, collecting data from government regulators and Grade 6 and Grade 7 contractors in Kota Kinabalu, Sabah. A questionnaire was administered to 62 respondents and the data were analysed using PLS-SEM. PLS-SEM was selected for its flexibility in handling diverse data requirements and complex models, offering an alternative to covariance-based SEM. The findings underscored a noteworthy correlation between barriers and drivers of BIM implementation, suggesting that overcoming obstacles is closely tied to advancing factors facilitating BIM adoption. Among the barriers examined, "Lack of direction of BIM in the industry" within the "Process" category emerged as significantly associated. Additionally, the study identified influential driving forces, both internal and external, which serve as catalysts for effective BIM integration. Notably, "Organizational culture that motivates BIM collaboration in projects" within the "Internal Push" category stood out as a significant driving factor.

Islamic Design Value: The Criteria of Design Pleased by Allah Based on Islamic Perspectives

Nor Ziratul Aqma Norzaman — 2025-06-26

The primary purpose of good design is to fulfil consumers’ needs and wants. This is because the global market has always been increasingly competitive than ever. Nevertheless, Muslims should always prioritise Allah SWT in every aspect of their lives, including in design. Unfortunately, this topic is rarely discussed and explored. Hence, this paper analyses the criteria of design based on Islamic perspectives, one that pleases Allah SWT, the Almighty God. Face-to-face interviews were conducted among Islamic scholars to explore their views on design criteria that align with Islamic principles. The findings were analysed using ATLAS.ti software, a qualitative data analysis tool, to systematically organize and interpret the qualitative data obtained from the interviews. The study highlights the importance of considering sustainability, ergonomics, aesthetics, Sharia compliance and fulfilment of human needs in design practices guided by Islamic principles. The findings serve as a guideline for designers, particularly Muslim designers, to create designs that please Allah SWT and contribute to a more inclusive and culturally sensitive design landscape. By integrating ATLAS.ti software into the analysis process, this study ensures rigor and transparency in the qualitative data analysis, thereby enhancing the credibility and reliability of the research findings. Overall, the paper contributes to the discourse on design theory and practice within Islamic perspectives.

Missing Wind Speed Data Prediction using Artificial Neural Network

Sayed Saad Ali Shah Mukaramshah — 2025-06-26

In southern Thailand, where monsoon-driven storms disrupt meteorological data collection, missing wind speed records hinder accurate weather forecasting and renewable energy planning. This study leverages an Artificial Neural Network (ANN) to impute missing wind speed data from the ERA5 reanalysis dataset (2012–2024) at latitude 6.0, longitude 102.0, addressing gaps that reduce forecasting precision by up to 12%. Using 114,792 hourly observations of wind speed, temperature, pressure, and precipitation, the ANN model, with 2–3 hidden layers and ReLU activation, was trained on 70% of the data, validated on 15%, and tested on 15%, incorporating 5% simulated missingness. The model achieved a Mean Absolute Error (MAE) of 0.42 m/s, Root Mean Squared Error (RMSE) of 0.58 m/s, and R² of 0.87, outperforming linear interpolation (MAE: 0.78 m/s). Feature importance analysis revealed precipitation as the dominant factor (40%), followed by temperature (25%) and pressure (20%). These results enhance storm forecasting for 2 million residents in Yala, Narathiwat, and Pattani and support Thailand’s 2,000 MW wind energy potential. Despite reduced accuracy during extreme storms (RMSE: 0.65 m/s), the ANN offers a robust solution for tropical data imputation, advancing meteorological research and sustainable energy applications. Future work should integrate real-time storm data and hybrid ANN-LSTM models to further improve predictions.

Dataflow Management (Architecture) with Model-Based Design for SAR Signal Processing on FPGA

Atthawut Tirakitpanitchakorn — 2025-06-26

The signal processing in ground-based synthetic aperture radar (GB-SAR) has two main processes. One is the pre-processing data to prepare the data; the other is an algorithm to establish the single-look complex (SLC) image. Normally, the pre-processing data process wastes time recording huge signals for processing and a nested loop inside the process. In this paper, we propose a dataflow management approach for the pre-processing data process to overcome the wasted time on recording data. Against the nested loop, we proposed model-based design (MBD) techniques that support the Hardware Description Language (HDL) coder in Simulink. The proposed method performs well without a nested loop and can be implemented on the Xilinx Zynq Z-7020 Field Programmable Gate Array (FPGA) board, which is a low-cost FPGA. From the result, our proposed method has a high average percentage error in some stages, but the output from the final stage gives a very low average percentage error. The data after our proposed method can give the SLC image, which has the same significance as the SLC image from the original method. Our proposed implementation can perform on the FPGA as well. Therefore, the proposed method using dataflow management with MBD gives data with a low average percentage error and the SLC image similar to the original method.

Digital Health Education for Tuberculosis Patients: A Systematic Review of Self-Care Improvement via Mobile Applications

Lilis Lismayanti — 2025-06-26

Tuberculosis (TB) remains a significant global health challenge, ranking among the leading causes of mortality worldwide. Indonesia, as of 2023, has the second-highest number of TB infections globally, according to WHO. This study evaluates the effectiveness of mobile applications in enhancing self-care, particularly treatment adherence among TB patients. A systematic review was conducted using four databases which are PubMed, Google Scholar, ProQuest, and Dimensions—to analyze studies published between 2019 and 2023. A total of 51 articles were screened, and 7 met the inclusion criteria. Findings highlight that digital health education via mobile applications significantly improves self-care and treatment adherence among TB patients, offering a practical alternative to direct observation therapy. The review underscores the importance of such tools in supporting daily treatment regimens. Future research should examine diverse application features, focusing on intervention feasibility and cost-effectiveness.

The Implementation and Challenges Impact of E-Learning Among Asnaf Students in Perlis: Analyzing using SPSS AMOS

Wan Azani Wan Mustafa — 2025-06-26

The rapid expansion of e-learning in Malaysia, accelerated by the COVID-19 pandemic and national education policies, has highlighted significant disparities in digital access among marginalized communities, particularly the Asnaf students in Perlis. Despite national initiatives like CERDIK improving device accessibility, systemic challenges such as poor internet connectivity, limited digital literacy, and socio-economic constraints continue to hinder effective e-learning adoption. This study investigates the implementation and challenges of digital learning among Asnaf students, employing a quantitative survey of 105 Form 3 and Form 4 students from three secondary schools in Perlis. Data were collected through a structured questionnaire assessing awareness, engagement, and obstacles related to e-learning. Statistical analyses, including Confirmatory Factor Analysis (CFA), Composite Reliability (CR), and Discriminant Validity, were conducted using Statistical Package for the Social Sciences (SPSS), Analysis of Moment Structures (AMOS) (IBM SPSS AMOS) to ensure data reliability and construct validity. Results indicate that while 91% of students have access to digital devices, 71% struggle with unstable internet, 72% face technical difficulties, and 59% lack conducive study environments. CR (CR larger than or equivalent 0.70) confirmed internal consistency, yet engagement’s Average Variance Extracted (AVE) (0.38) fell below the threshold, signaling measurement limitations. This low AVE indicates weak convergent validity, suggesting that the engagement construct may not robustly capture students’ actual participation in e-learning. This study contributes to the discourse on digital equity by advocating for inclusive strategies that ensure sustainable and meaningful e-learning adoption among underprivileged students in Malaysia.