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International Journal of Advances in Applied Sciences
Published by Institute of Advanced Engineering and Science
ISSN : 22528814     EISSN : 27222594     DOI : http://doi.org/10.11591/ijaas
Core Subject : Science, Education, Social,
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics
International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.
Arjuna Subject : -
Articles 680 Documents
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Microcontroller-based camera with the sound source localization for automated accident detection Nazeem, Nur Nazifah Adlina Mohd; Hassan, Siti Lailatul Mohd; Halim, Ili Shairah Abdul; Abdullah, Wan Fadzlida Hanim; Sulaiman, Nasri
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp639-646

Abstract

This paper is on a microcontroller-based camera controller with sound source localization (SSL). With the rising frequency of highway accidents in Malaysia, there is a pressing need for a reliable detection system. The current approach, involving fixed-angled cameras, necessitates constant human monitoring, proving inefficient. To address this, the study introduces a hybrid camera system incorporating a camera for image capture and a microphone to detect collision sounds. By integrating a pan-tilt (PT) camera controller driven by time difference of arrival (TDOA) inputs, the system can swiftly move toward accident locations. The TDOA method is employed to convert sound arrival time differences into camera angles. The accuracy of the PT camera's rotation angle was analyzed based on the original sound source angle. As a result, this project produced an automated highway monitoring camera system that uses sound SSL to detect car crash sounds on highways. Its PT feature will help cover a large highway area and eliminate blind spots to capture possible accident scenes. The average inaccuracy of the experimental test of the pan and tilt angle of the camera is 19 and 23%, respectively. The accuracy of the pan tilt angle can be increased by adding more analog acoustic sensors.
Intelligent control strategies for grid-connected photovoltaic wind hybrid energy systems using ANFIS Babu, Thiruveedula Madhu; Chenchireddy, Kalagotla; Kumar, Kotha Kalyan; Nehal, Vasukul; Srihitha, Sappidi; Vikas, Marikal Ram
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp497-506

Abstract

This study proposes intelligent control strategies for optimizing the grid integration of photovoltaic (PV) and wind energy in hybrid systems using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS control aims to enhance grid stability, improve power management, and maximize renewable energy (RE) utilization. The hybrid system's performance is evaluated through simulations, considering various environmental conditions and load demands. Results demonstrate the effectiveness of the proposed ANFIS-based control in dynamically adjusting the power output from PV and wind sources, ensuring efficient grid-connected operation. The findings underscore the potential of intelligent control strategies to contribute to the reliable and sustainable integration of RE into the grid.
Employing transfer learning techniques for COVID-19 detection using chest X-ray Garg, Preeti; Gautam, Madhu; Chugh, Bharti; Dwivedi, Karnika
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp680-688

Abstract

Coronavirus 2 (SARS-COV-2) is a global emergency that continues to terrify the globe at an alarming rate. Some nations are still combating the virus, attempting to discover infected individuals early on to prevent the infection from spreading. In terms of identifying the pattern in the pictures, radiological patterns have been shown to have greater accuracy, sensitivity, and specificity. Publicly available datasets are used for the implementation. The data is divided into three categories: COVID, normal, and pneumonia patients. Transfer learning is a type of deep learning that allows pre-trained models to be used and achieves high accuracy by detecting various anomalies in limited medical datasets. An image dataset of 1109 pictures was used in this work, and training was done using two distinct models, ResNet50 and InceptionV3, to distinguish the patient categories. For ResNet and InceptionV3, the proposed model has an accuracy of 97.29 and 98.20, respectively, with a sensitivity of 100% for InceptionV3 and a specificity of 99.41% for ResNet50. With a 98.20% accuracy, complete sensitivity, and high specificity, this study presents a deep learning model that gives diagnostics for multiclass classification and attempts to discriminate COVID-19 patients using chest X-ray photos. Other illnesses can also be detected using the proposed model.
Performance evaluation and integration of distortion mitigation methods for fisheye video object detection Du, John Benedict; Mayuga, Gian Paolo; Guico, Maria Leonora
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp743-758

Abstract

The distortion observed in fisheye cameras has proven to be a persistent challenge for numerous state-of-the-art object detection algorithms, instigating the development of various techniques aimed at mitigating this issue. This study aims to evaluate various methods for mitigating distortion in fisheye camera footage and their impact on video object detection accuracy and speed. Using Python, OpenCV, and third-party libraries, the researchers modified and optimized said methods for video input and created a framework for running and testing different distortion correction methods and object detection algorithm configurations. Through experimentation with different datasets, the study found that undistorting the image using the longitude-latitude correction with the YOLOv3 object detector provided the best results in terms of accuracy (PASCAL: 68.9%, VOC-360: 75.1%, WEPDTOF: 15.9%) and speed (38 FPS across all test sets) for fisheye footage. After measuring the results to determine the best configuration for video object detection, the researchers also developed a desktop application that incorporates these methods and provides real-time object detection and tracking functions. The study provides a foundation for improving the accuracy and speed of fisheye camera setups, and its findings can be valuable for researchers and practitioners working in this field.
Determining the retail sales strategies using association rule mining Yanti, Roaida; Maradjabessy, Prita Nurkhalisa; Qurtubi, Qurtubi; Rachmadewi, Ira Promasanti
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp530-538

Abstract

Competitive competition in the retail industry requires retailers to maintain improvements and formulate accurate strategies to maintain their competitiveness. A small number of daily visitors visit retail store Y if compared to other retail stores, which leads to decreased store revenue due to the small number of products sold. Therefore, it is crucial to formulate the right business strategy to increase sales by utilizing customer shopping behavior derived from transaction data. The method used is association rule mining (ARM) with a frequent pattern growth (FP-growth) algorithm to determine consumer buying patterns. Data processing results generate five valid rules that meet the specified criteria for an association relationship. Utilization rules are acknowledged by determining retail sales strategies by recommending store layouts, shopping catalogs, and voucher discounts to attract customers.
Fundamental frequency extraction by utilizing modified BaNa in noisy speech Saha, Arpita; Parvin, Nargis; Rahman, Md. Saifur; Rahman, Moinur; Chowdhury, Any
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp515-529

Abstract

A sound’s pitch can be largely understood and perceived by using its fundamental frequency. Multiple algorithms have been developed for extracting fundamental frequency, and the choice of which one to employ depends on the noise and features of the signal. Therefore, for an accurate fundamental frequency estimate, the noise resistance of the algorithm becomes even more crucial. Still, many of the most advanced algorithms fail to produce acceptable results when faced with loud speech recordings that have low signal-to-noise ratios (SNRs). In this research paper, we focus on the harmonic selection step in BaNa method, which is one of the vital parts for enhancing the extraction accuracy of fundamental frequency (F0) in noisy situations. BaNa algorithm always emphasizes 5 harmonics on average for both male and female speakers. However, our observation reveals that relying on 5 harmonics is inadequate for male speakers in noisy conditions. Thus, we propose a new idea based on BaNa that separately utilizes the 3 harmonics for male speakers and 5 harmonics for female speakers to achieve accurate pitch extraction within noisy environments. The results demonstrate that our proposed approach attains the lowest rate of gross pitch error (GPE) across various noise types and SNR levels.
Healthy building phytoarchitecture requires essential criteria for sustainable phylloremediation of contaminated indoor air Samudro, Ganjar; Samudro, Harida; Mangkoedihardjo, Sarwoko
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp662-672

Abstract

Various ambient air contaminants can spread into the indoor building through air transport. With the additional generation of contaminants from indoor activities, indoor air quality (IAQ) has the potential to be polluted. Indoor air pollution incidents can occur anytime, which is difficult to predict. Therefore, it is necessary to take action to improve IAQ as early as possible and sustainably. The solution to sustainable remediation is using plants to apply phylloremediation, which functions as leaves and leaf-associated microbial communities to reduce air contaminants. This study aims to provide new practical yet essential criteria for the sustainable operation of phylloremediation. This review is based on the latest results of a literature-based study. An analysis of the fundamental processes of plant life forms the basis for obtaining these criteria. The study emphasizes key criteria for phylloremediation encompassing the selecting plants with high transpiration and leaf-microbe synergy, and conducting maintenance by spraying water on leaves. These measures optimize efficiency and sustain the process for indoor air pollutant reduction. The final result summarises the new criteria for sustainable phylloremediation to maintain plant life. These essential criteria can be used for conducting experiments in empirical research, indoor design, and education for the community.
Usability analysis of marker-based augmented reality application for the microcontroller study Beeran Kutty, Suhaili; Nazri, Muhammad Azim; Anuar, Che Nur Shafareen Afera Che; Kassim, Murizah; Sulaiman, Norakmar Arbain
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp707-716

Abstract

Virtual labs using augmented reality (AR) applications have brought transformation in teaching laboratory courses. However, in the field of engineering laboratories, the use of AR applications is still new. In this work, a marker-based AR application is used as a tool that enables a hands-on learning experience of microcontroller study. The software used to develop this application are Unity3D and Vuforia. C# programming language has also been used to provide the command for the interaction between the interface and the application. After the development of the application was complete, it was tested by a group of electrical engineering students. Then, the students are required to fill out a survey on the system usability scale (SUS) test. The SUS score of this application is 62.5. It was found that the perceived usability of the evaluated AR application as a teaching tool for laboratory courses is acceptable. This response shows the marker-based AR application has the potential to be used as a teaching aid in engineering laboratory courses.
Performance of the silica adsorbent from snake fruit peel for removing heavy metals of Ag, Cu, Mn, and Cr in SCW Salamah, Siti; Satar, Ibdal
International Journal of Advances in Applied Sciences Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i2.pp459-466

Abstract

Silver crafts wastewater (SCW) typically contains environmentally harmful heavy metals, including Ag, Cu, Mn, and Cr, necessitating treatment before disposal. This study explores a promising solution using silica (SiO2) adsorbents derived from snake fruit peel through acidic activation with HCl concentrations of 2, 4, and 6 M. Qualitative analysis of the adsorbent involved Fourier-transform infrared spectrometer (FTIR) and x-ray fluorescence (XRF) techniques. XRF analysis revealed major compositions of Si (26%) and Cl (71.46%), with minor elements such as Ca (0.91%), P (0.42%), K (0.37%), Fe (0.12%), and others. FTIR analysis indicated the presence of siloxane (Si-O-Si) and silanol (Si-OH) on the adsorbent. The SiO2 adsorbent demonstrated effectiveness in removing heavy metals (Ag, Cu, Mn, and Cr) from SCW, achieving removal percentages of approximately 16.96%, 24.38%, 19.34%, and 9.82%, respectively. This research contributes to the development of an environmentally friendly approach for SCW treatment using silica adsorbents derived from agricultural waste.
Enhancement performance of the Naïve Bayes method using AdaBoost for classification of diabetes mellitus dataset type II Mahendra, I Gusti Agung Putu; Wirawan, I Made Agus; Gunadi, I Gede Aris
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp733-742

Abstract

In using technology, especially in health sciences, machine learning modeling can make it easier to predict disease treatment. Naïve Bayes optimization with AdaBoost is needed because even though Naïve Bayes has the advantage of minimal parameters, its accuracy is susceptible to too many features. AdaBoost is used to overcome sensitivity to an excessive number of features and optimize its ability to handle complex datasets. This research aims to analyze the classification results of the Naïve Bayes method with the help of the AdaBoost method. This data comes from Community Health Centers I, II, and III Mengwi District, Bali Province patient medical records. The classification process uses the Naïve Bayes method and Naïve Bayes with AdaBoost, which is then evaluated using a confusion matrix. Two scenarios were used in testing: Naïve Bayes and AdaBoost-based Naïve Bayes. The algorithm is implemented on the dataset and tested directly using cross-validation. The evaluation results show that the Naïve Bayes method experienced an increase in accuracy of 5.92% at 5-fold and 5.93% at 10-fold on a dataset with 890 data. The addition of the AdaBoost method to diabetes classification has been proven to improve the accuracy performance of the Naïve Bayes method.

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