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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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Google Play review analysis of digital mobile applications of Islamic microfinance institutions Alam, Azhar; Ratnasari, Ririn Tri; Nurrochman, Danang Dwi; Rahmawati, Estina
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.pp717-726

Abstract

The study aims to analyze user reviews of Islamic microfinance mobile applications to evaluate their effectiveness for micro, small, and medium-sized enterprises (MSMEs). Using the netnography method, this study collected and analyzed 4,131 reviews from the Google Play Store, focusing on applications with ratings above 4.5. The data was categorized into positive and negative reviews. Key findings indicate that users appreciate 50.42% of positive reviews expressed satisfaction and motivation to advance, 28.90% praised the ease of transactions via the application, and 20.68% appreciated the practical benefits in any payment, while with frequent errors (25.53%), issues with activation codes (21.28%), and transaction failures (17.02%) being the most common complaints. The study recommends improving technical reliability to enhance user satisfaction. Future research should explore user experiences in more diverse digital environments. This research contributes to understanding user perceptions and strengthening the development of Islamic microfinance applications.
Effect of spacing, concentration of NaCl solution, and biasing of graphite electrodes towards conductometric sensor response Zulkarnain, Izzah Wadhihah; Abdullah, Wan Fazlida Hanim; Halim, Ili Shairah Abdul; Muslan, Muhammad Izzat Alif
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.pp620-627

Abstract

An electrical conductivity (EC) sensor is a conductometric sensor used to measure a solution's ability to transmit electrical charges. However, EC sensing accuracy and stability are not consistent due to many factors, such as gap spacing between electrodes, concentration of the solution, and electrical biassing. This study investigates the influence of the gap spacing between electrodes, the concentration of the solution, frequency, and voltage input applied to the EC sensor electrode on EC sensor measurement and provides insights into the relationship between these parameters and the sensor's performance using the voltage divider rule which is the simpler way to measure the conductivity of the solution. From this investigation, gap spacing between the electrodes, the concentration of the solution, frequency below 50 Hz, and voltage input have been found to directly affect the EC sensor measurement. However, there is no significant change in EC sensor measurement regarding the frequency applied to graphite electrodes when the frequency is above 50 Hz. The findings of this study highlight the complex interplay between the physical setup parameters and EC sensor measurement.
Optimization of mechanical properties of Al7150/Si3N4/C composites using artificial neural network Zakaulla, Mohamed; Pasha, Younus
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.pp556-565

Abstract

The study aims to investigate and predict the effect of reinforcements such as silicon nitride (Si3N4) and graphene (C) in aluminum 7150 matrix. Al7150/Si3N4/C hybrid composite is fabricated by a stir casting technique and subsequently T6 heat treated for applications such as body stringers, spar chords, seat tracks, and stringers of wing surfaces of aircraft. A feedforward propagation multilayer neural network was developed for modeling and prediction of hardness, tensile strength, and tensile elongation. The results show that the addition of fillers and T6 heat treatment enhances the mechanical properties of the Al7150/Si3N4/C composite. The artificial neural network (ANN) model suggested for Al7150 composites demonstrates beneficial results when compared to experimental measurements. The prediction model, which has a mean absolute percentage error of 0.64%, 0.3%, and 2.49% for hardness, tensile strength, and tensile elongation can accurately predict the effect of reinforcement contents and T6 heat treatment on mechanical properties of Al7150/Si3N4/C composites.
Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm Razali, Nur Syifa Nasyrah; Yasin, Zuhaila Mat; Dahlan, Nofri Yenita; Noor, Siti Zaliha Mohammad; Ahmad, Nurfadzilah; Hassan, Elia Erwani
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.pp647-654

Abstract

An energy storage system called a battery energy storage system (BESS) collects energy from various sources, builds up that energy, and then stores it in rechargeable batteries for future use. The battery's electrochemical energy can be discharged and supplied to buildings such as residences, electric cars, and commercial and industrial buildings. The advantages of utilizing BESSs, such as minimizing energy loss, improving voltage profile, peak shaving, and increasing power quality, may be reduced if incorrect decisions about the appropriate position and capacity for BESSs are chosen. Furthermore, the optimal position and size for BESSs are critical since deploying a BESS at every bus, particularly in an extensive network, is not a cost-effective option, and installing oversized BESSs would result in higher investment expenses. Hence, this study suggests a proficient method for identifying the most suitable position and the sizes of BESS to save costs. The grasshopper optimization algorithm (GOA) and evolutionary programming (EP) were employed to address the optimization challenge on the IEEE 69-bus distribution test system. The goal of the optimization is to minimize the overall cost. The findings indicate that the GOA has strong resilience and possesses a superior capacity for optimizing cost reduction in comparison to EP.
Signal processing for abnormalities estimation analysis Razman, Nur Fatin Shazwani Nor; Nasir, Haslinah Mohd; Zainuddin, Suraya; Brahin, Noor Mohd Ariff; Ibrahim, Idnin Pasya; Mispan, Mohd Syafiq
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.pp600-610

Abstract

Pneumonia, asthma, sudden infant death syndrome (SIDS), and the most recent epidemic, COVID-19, are the most common lung diseases associated with respiratory difficulties. However, existing health monitoring systems use large and in-contact devices, which causes an uncomfortable experience. The difficulty in acquiring breathing signals for non-stationary individuals limits the use of ultra-wideband radar for breathing monitoring. This is due to ineffective signal clutter removal and body movement removal algorithms for collecting accurate breathing signals. This paper proposes a breathing signal analysis for non-contact physiological monitoring to improve quality of life. The radar-based sensors are used for collecting the breathing signal for each subject. The processed signal has been analyzed using continuous wavelet transform (CWT) and wavelet coherence with the Monte Carlo method. The finding shows that there is a significant difference between the three types of breathing patterns; normal, high, and slow. The findings may provide a comprehensive framework for processing and interpreting breathing signals, resulting in breakthroughs in respiratory healthcare, illness management, and overall well-being.
Optical character recognition for Telugu handwritten text using SqueezeNet convolutional neural networks model Revathi, Buddaraju; Raju, B N V Narasimha; Marapatla, Ajay Dilip Kumar; Veeramanikanta, Kagitha; Dinesh, Katta; Supraja, Maddirala
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.pp487-496

Abstract

Optical character recognition (OCR) is a process that recognizes and converts data from scanned images, including both handwritten and printed documents, into an accessible format. The challenges in Telugu OCR arise from compound characters, an extensive character set, limited datasets, character similarities, and difficulties in segmenting overlapping characters. To tackle these segmentation complexities, an algorithm has been developed, prioritizing the preservation of essential features during character segmentation. For distinguishing between structurally similar characters, we used convolutional neural networks (CNN) due to their feature-extracting properties. We have employed the CNN model, the SqueezeNet for feature extraction, resulting in an impressive character recognition rate of 94% and a word recognition rate of 80%.
Proportion optimization of honey pineapple juice on the custard organoleptic and chemical properties Susanti, Siti; Tiaswuni, Lutviana; Al-Baarri, Ahmad Ni’matullah; Rachma, Yasmin Aulia
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.pp655-661

Abstract

Custard is categorized as a dairy product that has a sweet taste, soft, and thick texture. The addition of honey pineapple to custard making is an innovation to improve the organoleptic and chemical characteristics of custard. The purpose of this study is to know the effect of adding honey pineapple juice on the organoleptic and chemical properties of pineapple custard. The experimental design used 5 treatments and 4 replications. The study began with the process of making custard products according to the addition of honey pineapple juice treatments there are P0 (control), P1 (20%), P2 (40%), P3 (60%), and P4 (80%). The test method is done by organoleptic and proximate analysis tests. The results of the organoleptic test showed that the addition of honey pineapple juice affects the color, taste, aroma, and overall preference of pineapple custard. Proximate analysis showed that the addition of pineapple juice honey affected the water content, ash content, fat content, and carbohydrate content of pineapple custard. This study concludes that the difference in the proportion of pineapple honey affects the organoleptic and chemical characteristics of pineapple custard.
Comparing 5G indoor wireless technologies: optical vs ultraviolet communication Zainal, Izzah Hazirah; Mutalip, Zaiton Abdul; Yusuf, Haziezol Helmi Mohd; Hassan, Nurmala Irdawaty; Rihawi, Zeina
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.pp611-619

Abstract

Wireless communication technologies have become one of the biggest technologies that are considered suitable for radio frequency in various types of applications, including indoor applications. This study investigates the differences between optical wireless communication (OWC) and ultraviolet wireless communication (UVC). Several parameters are simulated, including bit error rate (BER), signal-to-noise ratio (SNR), data rate, and received power, to compare the performance of each technology in various scenarios. Owing to its wide bandwidth, license-free frequency spectrum, and low signal attenuation at low carrier frequencies, the OWC is suitable for radio frequency applications in many situations, including indoor environments. The simulation results show that UVC outperformed OWC in terms of BER and SNR. Moreover, UVC has a higher data transmission rate of up to 1.1 Gbps, making it a suitable technology for 5G communication.
Hybrid load forecasting considering energy efficiency and renewable energy using neural network Aizam, Adriana Haziqah Mohd; Dahlan, Nofri Yenita; Asman, Saidatul Habsah; Yusoff, Siti Hajar
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp759-768

Abstract

In recent years, the relationship between a country's gross domestic product (GDP) and its electricity consumption has changed significantly due to increased energy efficiency (EE) and renewable energy (RE) adoption. This decoupling disrupts conventional load forecasting models, affecting utility companies. This study has developed an innovative solution using an artificial neural network (ANN) Hybrid method for load forecasting, resulting in a remarkably accurate model with 99.68% precision. Applying this model to Malaysia's electricity consumption from 2020 to 2040 reveals a significant 13% reduction when accounting for EE and RE trends. This method aids risk management, contingency planning, and decision-making by accurately reflecting changing energy usage dynamics influenced by EE and RE sources.
A brief on artificial intelligence in medicine Ounasser, Nabila; Rhanoui, Maryem; Mikram, Mounia; El Asri, Bouchra
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp1055-1064

Abstract

This review explores the transformative impact of artificial intelligence (AI) in medicine. It discusses the benefits of AI, its core technologies, integration processes, and diverse applications. AI enhances diagnostics, personalizes treatments, and optimizes healthcare operations. Machine learning and deep learning are key AI technologies, while explainable AI ensures transparency. The review emphasizes the integration journey and highlights AI applications, from image diagnosis to telemedicine. Ethical concerns, data privacy, regulations, and algorithmic bias are challenges. The future promises continued innovation, global health equity, and responsible AI application in medicine.

Page 51 of 68 | Total Record : 680

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