Journal of Advanced Research Design

Tech-Infused Narrative: A Systematic Review of Digital Storytelling in Education

2025-04-08T16:37:26+07:00

Digital storytelling has revolutionized traditional teaching methodologies by merging technology and narrative, creating dynamic learning experiences. This systematic review examines "Technology-Infused Narratives," which emphasize the inseparable relationship between technology and narrative-based educational methods. Addressing a gap in comprehensive analysis, this study evaluates the implementation and impact of digital storytelling across educational levels and disciplines. Utilizing the PRISMA framework, literature from Scopus and ERIC was synthesized, with 30 articles selected for detailed analysis out of an initial 352. Findings reveal that digital storytelling enhances engagement, critical thinking and literacy skills, supporting multilingual learners and fostering creative pedagogy. It also facilitates cultural heritage preservation and promotes innovative teaching strategies. Despite its benefits, challenges such as story selection, legal issues and evaluating student learning persist, along with ethical considerations in social justice pedagogy. The study underscores the transformative potential of digital storytelling, advocating for its recognition as a versatile educational tool. Thoughtful implementation can maximize its effectiveness, addressing diverse learning needs and promoting active student involvement. This review provides a roadmap for educators and policymakers to navigate the integration of narrative-driven technology, highlighting the need for nuanced technology integration, collaboration and personalization in education. Ultimately, digital storytelling offers a significant departure from traditional methodologies, fostering a more engaging and effective educational environment.

Academic Integrity Elements using Technological Media Tools in Peer Assessment Implementation: Fuzzy Delphi

2025-03-21T14:25:01+07:00

In recent years, there have been many concerns about issues related to integrity and ethics among students when completing tasks using technological media tools. Peer evaluations indirectly influence early moral representations through other people's evaluations of their self-representations while interacting with others. This research aims to explore the element of academic integrity using technological media tools in peer assessment implementation. The expert participants for this Fuzzy Delphi study were selected using purposive sampling. A questionnaire was used to obtain the consent of 15 experts in the field of technology education and classroom assessment related to academic integrity elements using technological media tools in peer assessment implementation. The data from this research has been analysed using the Fuzzy Delphi formula. These results show that the five (5) elements of academic integrity: attitude, personality, behaviour, commitment and professional practice have been approved by experts as elements of academic integrity using technological media tools in the implementation of peer assessment. Therefore, peer assessment boosts students' confidence and encourages healthy discussion.

Efficient Application of Modified TOR Iterative Method with GPU Acceleration in Robotics and Image Processing

2025-04-06T06:38:03+07:00

This paper presents the application of the Modified TOR (MTOR) method, implemented using CUDA, for solving Poisson's equation through the iterative Finite Difference (FD) method. The study aims to provide a detailed description of the MTOR method for solving Partial Differential Equations (PDEs) on both standard and rotated meshes, employing CUDA for parallel processing. The MTOR method uses a red-black ordering strategy during iteration; consequently, the calculation of red cells utilizes the updated values of their four neighbouring black cells, and vice versa. To evaluate the performance of these iterative methods, robot path planning and image blending problems were tested. Our results highlight the advantages of employing parallel methods on Graphics Processing Units (GPUs) in terms of iterations and computational time, compared to their sequential counterparts. In the path planning simulation, the parallel implementations exhibited over 20 times faster execution compared to their corresponding sequential versions. Similarly, in the image blending problem, the parallel implementations achieved more than a 6-fold improvement in speed compared to the sequential methods.

Safeguarding Patient Data: Machine Learning for Phishing URL Detection in Healthcare Systems

2025-03-21T09:46:57+07:00

Since the healthcare industry depends more and more on digital infrastructure, it is a perfect target for cyberattacks especially phishing. Designed to pass for real healthcare websites, phishing URLs seriously compromise patient data security. Effective strikes may cause disruptions in patient care, financial losses, and Protected Health Information (PHI) breaches. This work investigates the use of machine learning (ML) approaches for robust and accurate phishing URL detection in healthcare systems in order to handle this important problem. We examine a Multilayer Perceptron (MLP) neural network-based detection model and evaluate its performance against known techniques, Decision Tree (DT) and Naive Bayes (NB). Comprehensive URL datasets—more especially, the ISCX-URL-2016 dataset for training and testing—as well as the CIC-InvesBanking-2017 dataset combined with live phishing feeds for validation help to train and rigorously validate the models. With a high accuracy of 87.45% on test data and a precision of 84% on unseen validation data, our experimental results show that the proposed MLP model much exceeds DT and NB. This emphasizes how ML—more especially, MLP—may improve cybersecurity defences in healthcare, hence securing private patient data and the integrity of healthcare processes.

Investigating the Efficiency of Papaya and Date Dry Seeds as Natural Coagulants in the Wastewater Treatment Process

2025-04-06T06:56:04+07:00

Industrial effluent contaminants include insoluble substances, heavy metals and organic and inorganic compounds. The textile industry has a hard time dealing with its waste because it contains a lot of different colours and chemicals. To treat industrial effluent, coagulation-flocculation is frequently employed due to its efficacy in removing suspended particles organic matter, turbidity and colour. Conversely, employing a chemical coagulant may result in significant costs and the production of considerable quantities of non-biodegradable waste and metallic byproducts in the treated water, both of which face contamination risks by pollutants and diseases. An environmentally sustainable, non-toxic and biodegradable alternative method under consideration of utilising natural coagulants derived from plants, namely papaya seed and date seed. This study uses different NaCl concentrations to see how well papaya seed and date seed work as natural coagulants in treating textile industry wastewater. NaCl was the solvent and distilled water was used to extract the natural coagulant. Chemical Oxygen Demand (COD), turbidity, ammonia nitrogen and pH were assessed to evaluate the textile wastewater sample's response to papaya and date seed purification. The experiment's findings indicate that papaya and date seeds possess exceptional coagulation properties. The optimum turbidity reduction efficiency for papaya seed was 61.48% when 30 ml of a 1.0 NaCl concentration solvent was utilised. On the other hand, date seed removed turbidity with an efficiency of 83.96% when 10 ml of a 1.0 NaCl solvent was utilised. Aside from that, 1.0M NaCl was found to be the most effective COD reduction solution for the Carica papaya seed, while 2.0M NaCl resulted in a 56.19% reduction for the date seed. In addition, 30 ml Carica papaya seed coagulant dosage containing 2.0M NaCl produced the most significant reduction in ammonia nitrogen (82.84%). Also, the dosage of 20 ml date seed coagulant containing 2.0M NaCl eliminated 43.33% of ammonia nitrogen. The study's findings illustrated the potential advantages of utilising papaya and date seeds as organic coagulants in the remediation of effluents from the textile industry.

Utilising Different Ratios of Plastic Waste Composition to Produce an Ecobrick as a Sustainable Building Material

2025-03-21T14:46:19+07:00

One of the most important building materials in construction is brick. Nevertheless, the production of brick brings more disadvantages than advantages to the environment and human health. Ecobrick has been identified as one of the tools that can be used as a sustainable building material in construction. In general, an ecobrick is a recycled Polyethylene Terephthalate (PET) plastic bottle filled with mixed inorganic waste such as used plastic, packaging or other wastes to a set density. The aim of this research is to determine the best ratio of plastic waste in PET bottles to produce ecobrick as a construction material. This research was conducted to evaluate the strength of the different density ratios of plastic waste in PET bottles in terms of compressive strength, drop impact resistance and flexural strength. A total of seven ecobrick samples with density ratios of 0 g/ml, 0.15 g/ml, 0.25 g/ml, 0.35 g/ml, 0.45 g/ml, 0.55 g/ml and 0.65 g/ml were prepared. The samples were tested using compressive tests, drop tests, flexural tests and failure analysis. The tests revealed that the optimum density ratio of an ecobrick is 0.45 g/ml. Subsequently, 3-unit and 6-unit blocks were developed to test the strength of the ecobrick with a density ratio of 0.45 g/ml. This was done to investigate the feasibility of ecobrick as a construction material. The compressive strength for the 6-unit ecobrick was 9272.13 N while the compressive strength for the 3-unit ecobrick was 3649.03N. It can thus be concluded that a combination of ecobrick units provides greater compressive strength which makes it suitable as construction material.

Signal Restoration using Bayesian Neural Network

2025-03-11T15:20:14+07:00

Signal restoration can be defined as predicting original input signal which being affected by the noise using some prior information from the process or the desired output signal. This paper deals with signal restoration using estimation by applying a Bayesian Neural Network (BNN). Signal recovery emphasizes the challenge of predicting the original input signal from the distorted or noisy version of the original signal. Data that was lost or went missing during transmission or storage must be recovered and added back to the original signal. The objective of this project is to restore the lost signal with a high degree of precision. Three datasets, notably those related to Energy, Air Quality, and Combined Cycle Power Plants, were predicted for this study. These datasets will be trained utilising the Bayesian Regularized (BR), Levenberg-Marquardt (LM), and Gradient Descent (GD) training algorithms. The Coefficient of Determination (R2) and Mean Square Error are used to gauge the effectiveness of signal restoration (MSE). In comparison to LM and GD, the results demonstrate that BR is the most effective training algorithm for signal restoration. In comparison to the LM and GD training algorithms, the majority of R2 values for BR are close to 1 (0.7 to 0.99) and the MSE value is the lowest (-32.9387 dB). This proposed BNN model can be apply in the signal restoration and prediction for practical wireless communication system such as speech and audio processing, multimedia processing, underwater acoustic, biological signal and others.

Sokoban: A Game-Based Learning Game for Enhancing Programming Skill Among Undergraduate Students in Malaysia

2025-03-12T08:28:28+07:00

Programming is a necessary skill for students in computer science-related fields, but teaching it to first-year undergraduates can be challenging. Traditional classroom-based learning and teaching methods are insufficient, especially when it comes to programming. Game-based learning has emerged as an effective way for students to learn programming, even though there is currently a lack of empirical evidence in the literature. Therefore, this research aims to develop a Game-based Learning (game-based learning) Computer Game that can effectively teach programming to undergraduate students. The game will be expertly reviewed by three game designers and three programming lecturers to ensure its effectiveness. The study will involve a quasi-experiment, in which students will play the game while assessing their understanding of programming. One programming module (Object-Oriented Programming) will be covered and two batches of Year 1 Bachelor of Software Engineering (Educational Software) students from Universiti Pendidikan Sultan Idris will participate in this research. All participated students are randomly divided into control and focus groups. Data collection was according to A quasi-experiment with a control group that was conducted to investigate the effectiveness of Sokoban in learning programming. A comparative analysis using ANOVA method on the efficiency of learning programming between game-based learning approach and existing learning approach (among control group and experimental group) revealed great significance [F = 6.6296, p <.05]. Therefore, it can be concluded that the Sokoban computer game was effective in teaching programming to undergraduate year one students. These findings have significant implications for the future development of game-based learning games to aid undergraduate students in learning topics related to programming paradigm. The results suggest that game-based learning games could have a positive impact on programming education in Malaysian tertiary institutions. The knowledge gained from this study could contribute to determining the importance of the game-based learning approach for other ICT-related modules. The proposed game-based learning game is beneficial to academics, industry professionals and students in effectively learning programming.

Enhancing Parking Systems with QR Code-Integrated Automatic License Plate Recognition through Convolutional Neural Networks

2025-04-18T15:43:33+07:00

This abstract describes the development and evaluation of an Automatic License Plate Recognition (ALPR) system designed to simplify the process of parking ticket generation. The traditional paradigm of manual entry of license plate information by parking personnel for exiting vehicles is replaced by the automated system proposed in this study. The system integrates a YOLO (You Only Look Once) model for the automatic recognition of license plates in vehicle images. After this initial identification, a series of pre-processing and image segmentation techniques are applied to isolate and recognize the individual digits within the license plate. A ResNet model is then used to classify the license plates. The research focuses specifically on Malaysian license plates. The experimental results show that the YOLO model recognizes license plates robustly and accurately and has a high degree of reliability. However, when validating the data set, the ResNet model achieves an accuracy of around 80 %. The study points out inherent challenges, including potential errors in segmentation, problems with non-standardized or damaged tags, and the presence of digits that may have visual similarities. In summary, while the YOLO model is reliable in recognizing license plates, the classification accuracy of the ResNet model can be further improved. Overcoming challenges such as segmentation noise and variations in license plate conditions could further optimize the overall performance of the system.

The Effect of Soaking Treatment on Physicochemical Properties of Kenaf Seeds MH8234

2025-04-08T16:27:47+07:00

Kenaf seeds are considered to be one of the underutilised biomaterials generates from kenaf industry. These kenaf seeds contain essential nutrients, including dietary fibre, protein, fat, carbohydrate and phytocompounds. Consequently, they have attracted the attention of many researchers. This study aims to determine the effect of soaking treatment on the physicochemical properties of kenaf seeds MH8234. The soaking treatment can give many benefits such as easing the blending process, maximising the yield production of kenaf extract (kenaf seeds milk), improving the kenaf extract’s nutritional values and decreasing the presence of anti-nutritional compounds in the seeds. Twenty grams of kenaf seeds were manually cleaned to eliminate foreign debris and soaked in excessive distilled water. Soaking was carried out for 3, 6 and 12 hours in ambient conditions. The kenaf seeds were soaked before being analysed for size distribution, proximate analysis, water absorption, colour difference and microscopic appearance. Results showed that 12-hour soaking significantly increased seed size, moisture content (16.09%) and crude protein (20.14%), while slightly decreasing crude fat, ash content and total carbohydrates. Water absorption capacity ranged from 46.57% to 81.79%. The colour difference between soaked and unsoaked seeds was notable, shifting from dark brown to light brown. Overall findings suggested that a 12-hour soaking treatment demonstrate the highest water absorption capacity that enhanced seed size, protein content, as well as pleasant colour development, making it suitable for domestic and commercial practice.

Hybrid Human Interface System for Stress Level Monitoring: Integrating EEG and HRV Sensors

2025-03-25T13:44:39+07:00

This study addresses the pressing need for advanced stress monitoring systems by proposing a Hybrid Human Interface System. Currently, the accurate assessment of an individual's stress levels is a critical aspect of healthcare, wellness and performance optimization. However, existing stress monitoring approaches often lack the precision required for comprehensive evaluations. To bridge this gap, our study leverages Electroencephalogram (EEG) and Heart Rate Variability (HRV) sensors to create a sophisticated hybrid system. The EEG sensor captures intricate brainwave patterns, offering valuable insights into cognitive responses, while the HRV sensor measures the variability in heartbeat intervals, reflecting autonomic nervous system activity. The integration of these physiological data sources aims to provide a comprehensive and accurate assessment of stress levels, addressing the limitations of current methodologies. By combining these two sources of information, our proposed system enhances the precision and reliability of stress level assessments. The study concludes by highlighting the promising potential of the hybrid approach for advancing stress monitoring systems, with broad applications in healthcare, wellness and performance optimization.