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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.
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Articles 30 Documents
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Search results for , issue "Vol 13, No 3: September 2024" : 30 Documents clear
A model for classifying breast masses in ultrasound images Morsy, Shereen Ekhlas; Abd-Elsalam, Neveen Mahmoud; Al-Saidy, Zaid Abdu; Kandil, Ahmed Hesham; El-Bialy, Ahmed Mohammed; Youssef, Abou-Bakr Mohammed
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.pp566-578

Abstract

The most frequent type of cancer among women is breast cancer. Artificial intelligence (AI) researchers are developing automated systems to assist in the detection and classification of breast cancer. This study explores machine learning (ML) and deep learning (DL) as two AI methods for identifying benign and malignant breast tumors in ultrasound images. The investigation assesses the performance of various computer-aided detection and diagnosis (CAD) systems, which utilize either handcrafted features or deep features extracted from DL models. Furthermore, three models for CAD deep learning-based systems were implemented using convolutional neural networks (CNN), convolutional autoencoders (CAE), and deep features with CNN models, and compared with three traditional ML models based on handcrafted (texture) features. The results indicate that the deep features of the CNN model are promising, achieving a mean accuracy of 95% with a standard deviation of 1.1%.
User interface design of a sengkedan concept-based digital test Adiarta, Agus; Divayana, Dewa Gede Hendra; Ariawan, I Putu Wisna; Suyasa, P. Wayan Arta; Andayani, Made Susi Lissia; Wiradika, I Nyoman Indhi
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.pp478-486

Abstract

This study’s purpose was to demonstrate the design of a digital test for the “educational evaluation” course based on the sengkedan (swales) concept that has good quality. This research approach was a development that used the Borg and Gall model with more focus on the design development stage, initial design trials, and revisions. Subjects involved in the initial testing of the digital test user interface design were 42 respondents. The tool used to conduct initial testing of the digital test user interface design is a questionnaire. The data analysis technique used in this research was descriptive quantitative. The results showed user interface design of a sengkedan concept-based digital test for the “educational evaluation” course was quite good. The impact of this research on evaluators in the education field was a positive thing that added to their insights in developing a digital test. The evaluators will finally understand the importance of designing the user interface before finalizing the physical application to minimize errors.
A study of smart charging for electric vehicles using constant-current and constant-voltage technology Sutikno, Tole; Wahono, Tri; Ardiansyah, Ardiansyah
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.pp591-599

Abstract

The role of electric vehicles (EVs) is very important in the coming years because of their environmental friendliness and ability to absorb excess electricity from renewable energy sources (RES). Charging EVs will have an immediate negative impact on the power grid. EV smart charging provides a solution to these problems. For sustainable energy management, smart charging technology offers significant advantages in terms of faster charging times and optimized grid usage. By leveraging advanced algorithms and real-time communication capabilities, smart chargers enhance the efficiency, convenience, and environmental sustainability of EV charging infrastructure. Constant-current (CC) and constant-voltage (CV) technologies are essential components of smart charging systems, contributing to improved charging efficiency, battery safety, and grid stability. By regulating the charging process and optimizing power flow, these technologies play a crucial role in advancing the adoption of EVs and promoting sustainable energy management. When advanced CC-CV technologies are added to smart charging systems, the whole paradigm changes. Charging efficiency goes up by 40%, charging time goes down by 50%, and the grid's impact is reduced by 50% through better energy distribution.
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.

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