Journal of Advanced Research in Applied Sciences and Engineering Technology

Smart Object Detector for a Small-Scale Recycling Automation System

Mon, 07 Oct 2024 00:00:00 +0700

The increasing amount of waste generated each year necessitates immediate action to address and resolve this problem. This research establishes a small-scale recycling automatic system that allows us to detect objects based on different materials. The system is known as the Smart Object Detector for Small-Scale Recycling Automation System. Three types of objects, paper, metal, and plastic will be used to test the capability of the proposed system. The system will be equipped with the IR sensor, the capacitive proximity sensor, and the inductive proximity sensor. The conveyor belt is built, a servomotor is used to move the conveyor belt, and the containers are developed as part of a small-scale recycling automatic system conveyor system. The proposed smart object detector for a small-scale recycling automation system is found to be reliable for detecting, identifying, and segregating the object for different materials.

Soil Water Characteristics Curves (SWCC) of Residual Soils Stabilized using Emerging and Novel Bioinspired Technique

Mon, 07 Oct 2024 00:00:00 +0700

The development of unsaturated soil mechanics stemmed from the inadequacies of classical soil mechanics in addressing the partial pore spaces found in real-world soil systems. However, assessing soil parameters under partial saturation is resource-intensive. To streamline this, the Soil-Water Characteristic Curve (SWCC) theory was introduced. Widely applied across geotechnical engineering, water resources, and agriculture, SWCC predicts parameters of unsaturated soil systems. In geotechnical engineering, SWCC is vital for designing systems near the Earth's surface, like slopes, clay liners and foundations. Furthermore, most of conventional soil improvement techniques pose a lot of environmental concerns. thus, emergence of green construction materials widely known as bio mediated and bioinspired soil improvement techniques like Enzymatic induced calcite precipitation (EICP). EICP, an emerging eco-friendly as it employs use of simply chemical compound that reduces carbon footprints, bioinspired technique as it mimics natural forming process, was applied to stabilize tropical soil for SWCC determination. SWCC parameters were derived by subjecting EICP-treated soils at varying concentration of cementation solutions (0 – 1.00 M) using pressure plate method. Results revealed that saturated water content θs, retention water content θr, and air entry value Ψa increased with higher cementation solution concentration. The values of θs, θr, and Ψa improved from of 0.663, 0.23405, and 3.4832 at 0 concentration of cementation solution to 0.701, 0.22864, and 7.7086, respectively upon treatment with 1.00 M cementation solution. Finally, it has been demonstrated EICP is capable of improving the SWCC parameters of residual soil, thus the EICP technique can be exploited in stabilization of residual for construction of compacted clay liner.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Zulkarnain Zainal, Azizol Abdullah, Fahrul Hakim, Muhammad Daniel Hafiz Abdullah — Mon, 07 Oct 2024 00:00:00 +0700

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

An Integrated Approach: Watershed Segmentation with Local Maxima and Minima Algorithms for Tree Crown Delineation of Mango (Mangifera indica) using UAV Multispectral Imagery

Mon, 07 Oct 2024 00:00:00 +0700

Precision forestry and agriculture rely on precise tree crown delineation as a fundamental component for effective forest management activities. The selection of appropriate techniques and algorithms for tree crown delineation significantly influences the accuracy and reliability of the outcomes. This study was conducted in a mango plantation at Universiti Teknologi MARA, Arau, Perlis, employing Unmanned Aerial Vehicle (UAV) multispectral imaging. The focus is to assess the accuracy of tree crown delineation using two different algorithms, namely watershed segmentation and local maxima and minima. The conventional method was used for reference parameter derivation and tree positioning, while object-based image analysis was employed for processing the multispectral images. Additionally, a manual digitization approach was utilized to conduct an accuracy assessment of the resulting tree crown delineation. The study findings indicated that the watershed algorithm exhibited superior accuracy, achieving 83.3% total 1:1 match and 73% goodness of fit, compared to the local maxima and minima algorithm, which yielded 81.7% and 71%, respectively. These results hold significant implications for forest management planning and remote sensing-based forest quantification estimation, enabling the development of efficient and effective forest management strategies for the future.

Environmental Impacts on the RSS-Based Visible Light Indoor Positioning System

Foong Jun Kit, See Yuen Chark, Hadi Guna, Harry Ramza — Mon, 07 Oct 2024 00:00:00 +0700

To address the shortcomings of the global positioning system (GPS) operating within indoor scenarios, a range of indoor positioning systems (IPS) have been proposed. Among these, visible light IPS garners substantial research interest for its potential as a cost-effective IPS solution. However, susceptibility to environmental influences like external light and non-line-of-sight that can significantly affect its performance hindered its adoption. Hence, a simulation framework capable of simulating these influences becomes crucial in supporting the effective deployment and promoting the adoption of visible light IPS. This paper investigates the feasibility of using a simulation framework to replicate environmental factors affecting the system, including external optical sources and blocked transmitters. The research involved the development of a received signal strength (RSS) based visible light IPS prototype and an associated simulation framework. Subsequently, the prototype was tested in three distinct environments: no influences, with an external optical source, and non-line-of-sight. The prototype achieved an accuracy of 0.0836 m, 0.3541 m, and 1.1519 m for 50% of the time respectively in these three environments. Ultimately, the simulation framework developed is capable of reproducing similar effects for all three environments with a difference in the accuracy of 76.08%, 74.86% and 867%, respectively. Thus, the findings support the use of the developed simulation framework for the effective development of visible light IPS in environments with no influences and with external optical sources.

Potholes Detection in Doppler Radar Signal’s Power Spectral Density using Decision Tree

Mon, 07 Oct 2024 00:00:00 +0700

Potholes are defects on the surface of roads, streets, or pavements brought on by depressions or holes, which are hazardous for vehicles and pedestrians, whether small divots or huge craters. Various methods have been explored to improve the accuracy of potholes detection. Existing approaches have advantages and disadvantages. This paper presents the proposed method of pothole detection utilising Doppler Radar signal's Power Spectrum Density (PSD) together with the Decision Tree classification algorithm. While continuous waveform (CW) radar is able to identify moving targets, it cannot localise the exact depth of the reflector, which is the prominent characteristic of potholes. In addition, the target's reflected signal is likely to be masked by nearby harmonics. Since the radar is moving despite the target of interest, mounting it on a moving vehicle offers a different perspective. This paper explores the potential of Doppler radar's signal for pothole detection while comparing two Machine Learning (ML) techniques. A commercially over-the-shelf (COTS) K-LC2 Doppler radar was employed to acquire pothole and non-pothole raw datasets. Doppler signal was hardly distinguished between pothole and non-pothole, either in the time or frequency domain. Hence, Doppler signals were converted to power spectral density (PSD), and PSD's features were extracted. Extracted features were applied with the coarse Decision Tree (DT) and K-Nearest Neighbours (KNN) classification algorithms. The result exhibits a better accuracy of 91.2% for 80:20 distribution by using the Decision Tree.

Bioactive Compounds and Antioxidant Activity in Various Citrus Peels: A Significant Systematic Review

Mon, 07 Oct 2024 00:00:00 +0700

Citrus peels, traditionally considered byproducts in fruit processing, have garnered increasing attention due to their bioactive compounds and antioxidant characteristics. This systematic literature review aims to comprehensively evaluate and consolidate existing research on the bioactive compounds and antioxidant properties found in various citrus peel varieties. Citrus fruits, particularly their peels, have emerged as substantial repositories of bioactive compounds, including flavonoids, phenolics, and essential oils. Nevertheless, the fragmented nature of existing research on various citrus species, cultivars, and extraction methods underscores the need for a systematic approach. This is essential to provide a cohesive understanding of the inherent bioactive profiles and antioxidant capabilities present within these peels. Employing the Pre-Recording Systematic Reviews and Meta-Analysis (PRISMA) approach, this study executed a systematic search using academic strategies on Scopus and Web of Science databases to identify and select pertinent studies. Advanced search strategies utilizing keywords (1) citrus peel and (2) antioxidant was employed. A rigorous evaluation of methodological and analytical procedures was undertaken, and data were systematically collected for analysis, resulting in a final dataset of studies. This systematic review covered three key themes: (1) Citrus Fruit and Peel Utilization for Health and Nutrition, (2) Anticancer Properties and Chemical Composition of Citrus Products, and (3) Citrus By-Products and Waste Utilization. The review reveals substantial variability in bioactive compounds and antioxidant activities across different citrus peels. This review offers a comprehensive examination of each study's methodology and findings. Ultimately, the findings emphasize the importance of citrus peels as abundant sources of bioactive compounds and antioxidants, with far-reaching implications for nutrition, health, and biotechnological applications. While providing a comprehensive overview of the diverse bioactive profiles in different citrus peels, this review also advocates for further research to unlock their full potential and encourage sustainable practices in the food industry.

Preliminary Modelling of Hydrological Performance for Green Roof Drainage Layer using Response Surface Methodology

Salinah Dullah, Hidayati Asrah, Movierawanna Bernard, Jane Ann Jumi — Mon, 07 Oct 2024 00:00:00 +0700

Response Surface Methodology modelling is used in this study to investigate experimental result for waterproofing and drainage layers with slope ranging from 0% to 6% test beds with three types layers of drainage, rubber crumbs, oil palm shells and polyform. The aim of this study is to determine the accuracy and effectiveness of RSM through investigating the hydrological performance of green roofs. Nine sets of experimental data were used to analyse, and the input parameters include type of material drainage layer, slopes, and water absorption of the materials. The output variables were hydrograph and peak runoff, peak attenuation, and water retention for each material. It was proven that the mathematical equations developed by the RSM model can predict the output response, with ANOVA analysis being used to determine the level of significant effect of the input parameters on the green roof hydrological performance. RSM model's 2D contour plot and 3D surface plot expecting to revealed slope and water absorption. This modelling showed significant effect on peak runoff, peak attenuation, and water retention. It is proven that the RSM can be used to investigate various factors affecting green roof hydrological performance.

Precognito: The Emergence of Blockchain & Machine Learning-Based for Student Record Authentication System

Mon, 07 Oct 2024 00:00:00 +0700

Education is advancing faster than ever before as a result of technological advancements. Two issues brought on by technological advancements are fake transcripts and fake degrees, both of which are mostly due to data security flaws. Despite technological advancement, many domains have not been sufficiently investigated, and there will always be room for improvement. This project aims to create a tool that combines two cutting-edge technologies, machine learning (ML) and blockchain, to combat problems like degree and transcript forgery. By storing student data on the Blockchain and leveraging machine learning techniques for precise analysis, the technology can prevent additional fraud and uncertainty in student achievements and can enable accurate prediction of future employment opportunities for graduates. Machine learning algorithms are used to train and make accurate predictions, and the requisite data are retrieved from a Blockchain ledger. PRECOGNITO will equip the institution with a decentralized and immutable alumni database that contains verified and transparent academic records. Furthermore, this system gives employers a place to check the legitimacy of their employees' academic credentials. Moreover, PRECOGNITO allows students to upload their academic credentials to social media and professional networking sites like LinkedIn. With this system, recruiters may easily locate verified student information.

Performance Analysis of Surface Defect Detection Algorithm of Aerospace Parts via Confusion Matrix Approach

Mon, 07 Oct 2024 00:00:00 +0700

This paper examines the development of surface defect detection algorithm of a real plastic injection moulding parts from an aerospace part supplier company. Plastic injection moulding has evolved into one of the most essential and commonly utilized polymer processing activities in the plastics industry today. The work being done attempts to accomplish three goals: identify the frequency of defective injection moulded parts produced, develop an algorithm to detect defective parts using image processing techniques, and evaluate the effectiveness of image processing techniques for quality inspection in terms of accuracy and processing time. According to the protocol, there are five phases to this project: fault frequency detection, technique selection for image processing, MATLAB coding development, MATLAB coding debugging, and automatic inspection analysis. In this project, the algorithm and Graphical User Interface (GUI) are generated using the MATLAB Simulink program. A total of 100 parts samples were inspected, and the accuracy attained was 96% while processing time was reduced by 8.81 seconds.

Design and Finite Element Analysis of a Thin Cross-Section Soft Continuum Manipulator

Mohamed Tahir Shoani, Mohamed Najib Ribuan, Ahmad Athif Mohd Faudzi — Mon, 07 Oct 2024 00:00:00 +0700

Soft continuum robots and manipulators (SCRaMs) are mechanisms having elongated bodies. Most SCRaMs employ a thick cross-section body which constrains their maximum bending ability. This paper presents the design of a soft continuum manipulator employing a thin body cross-section. The manipulator’s modular design consists of three sections to facilitate maintenance and parts replacement. The manipulator’s critical components were tested through finite element analysis (FEA) to validate their performance under stress during operation. The results indicated a maximum stress value of 5.35 e+06 N/m2, representing 14.5% of the intended fabrication material’s tensile strength (ABS), and the maximum deformation displacement was only 0.74 mm. The presented design and testing results pave the way for the development and use of the manipulator for future applications.

Big Data Analysis on Network Intrusion Detection using High Performance Deep Neural Networks

Rajendran Bhojan, Saravanan Venkataraman — Mon, 07 Oct 2024 00:00:00 +0700

The rapid evolution of the internet over decades has given rise to a significant increase in cyber-attacks, propelled by the growth of high-speed internet. This paper addresses the escalating threat by focusing on Network Intrusion Detection Techniques in big data environments, specifically utilizing high-performance deep neural networks for analysing intrusive data within the enhanced NSL-KDD dataset. The research emphasizes the application of the random forest algorithm to enhance accuracy in detecting intrusive attack data within large datasets. The proposed model is thoroughly evaluated using the enhanced NSL-KDD dataset, demonstrating superior performance compared to existing systems. The study contributes a comprehensive overview of the methodology, incorporating advanced techniques in deep neural networks and random forest algorithms. The results highlight the effectiveness of the proposed model in bolstering cybersecurity measures, offering a robust solution for the evolving landscape of cyber threats.

Optical Simulation Study for Non-Invasive Jaundice Application: Effect of Bilirubin, Melanosome, LED Radial Width Distribution and Optode Distance

Mon, 07 Oct 2024 00:00:00 +0700

One of the promising diagnosis devices is the non-invasive bilirubin detection for estimating newborn jaundice. From the light interaction through tissue perspective, less attention has been given to the effect of LED radial width distribution at different melanosome content and bilirubin concentrations. Besides, different optode (emitter-detector) spacing can affect the light measured at the detector. This paper aims to investigate the effects of three LED radial width distributions (30°, 40° and 60°) on three types of skin pigmentation (light-skinned Caucasians, well-tanned Caucasians and Mediterranean, and dark-skinned pigmented Africans) based on hyperbilirubinemia and excessive hyperbilirubinemia conditions by using MCmatlab to simulate a 3D cuboid shape of neonatal forehead tissue layers. To mimic the tissues model, optical properties such as absorption coefficient (μ_a), reduced scattering coefficient (〖'μ〗_s), and scattering anisotropy (g) were applied in every layer. Wavelength 470 nm was chosen since it is the most responsive wavelength at bilirubin’s absorption spectra. Two optode spacing (0.7 cm and 2 cm) were used to demonstrate the effect of LED radial width. Main findings revealed that for all three types of skin, 30° LED radial width distribution resulted different trends in 0.7 cm and 2 cm of optode spacing. At 0.7 cm, a higher percentage of incident light was collected at the detector, whereas diffuse reflectance was lower. However, at a 2cm distance, both diffuse reflectance and the percentage of incident light collected on the detector were lower. These measured outcomes in diffuse reflectance on light skin were increased by six times, and the incident light collected was one time larger than on tanned and dark skin, as light skin colour absorbs less light and therefore gives a better reflection. Different optode spacing will be investigated for future studies as this information can serve as valuable input when designing optical hardware for non-invasive jaundice detection.

Computational Bibliometric using VOSviewer: Analysis of the Development of Internet Financial Reporting Research

Mon, 07 Oct 2024 00:00:00 +0700

This study seeks to investigate and analyse several scientific data about Internet Financial Reporting research advancement. This research is also anticipated to serve as a resource for researchers in determining financial literacy-related VOSviewer-based research topics. In this investigation, data from articles indexed by Google Scholar were utilized. Data collection through Internet Financial Reporting, Web Disclosure, and Financial Disclosure keyword searches. Data was collected from 2013 to 2022. The results demonstrated that a decade of research data yielded 982 articles. Based on the collected data, it can be seen that the number of publications has been increasing every year. Nine clusters were identified after conducting a keyword analysis of online financial reporting publications with VOSviewer. The study that employs bibliometric analysis advances the status of Internet financial reporting, a research field, precisely through visualizing trending topics and emergent trends. The analysis results have also suggested some variables that can be used in future research, which is very useful for identifying the knowledge base and future research directions in this area.

Promoting Problem Solving Ability in Enhancing Creative Reasoning through Adaptive Digital Game-Based Learning among Chemistry Undergraduate Students

Mon, 07 Oct 2024 00:00:00 +0700

Effective teaching and learning can be highly contributed using technology. STEM education is proved to improve student’s creative reasoning ability. Creative reasoning through adaptive digital game-based learning (ADGBL) is expected to promote problem solving ability among students. Therefore, this study is aimed to analyse problem solving ability in enhancing creative reasoning through adaptive digital game-based learning among chemistry undergraduate students. Nine first year chemistry undergraduate students selected to take part in this qualitative research. All students were given modules to complete based on the technology incorporated learning module, namely ADGBL. All of them were asked to take part in an open questions survey form upon completing the module and the data were analysed using thematic analysis. As a result, students were able to prompt their problem-solving ability from simple chemistry problems using jigsaw puzzles and complicated chemistry problems while using operating robotic structures using wireless method which involves electronic and coding technology. Students are able to solve the module challenges creatively and effectively.