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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 40 Documents
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Search results for , issue "vol 15, no 1: march 2026" : 40 Documents clear
Integrating swarm intelligence with CMIP climate models for ecocritical environmental analysis R., Pavithra; Mahadevan, S.
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp168-177

Abstract

This research establishes a cohesive swarm intelligence framework used for climate simulations derived from the coupled model intercomparison project phase 6 (CMIP6), obtained from the earth system grid federation (ESGF). The study examines essential environmental variables such as near-surface air temperature (tas), sea-level pressure (psl), precipitation (pr), surface shortwave radiation (rsds), and longwave radiation (rlds). The system specifically evaluates a global mean surface temperature rise of 1.72 °C, a psl range of 980-1,030 hPa, pr anomalies averaging ±1.3 mm/day, rsds values fluctuating between 140-280 W/m², and rlds values reaching a maximum of 350 W/m² for high-emission shared socioeconomic pathways (SSP)5-8.5 scenarios. The characteristics served as inputs for decentralized particle swarm architecture aimed at identifying ecological stress signs via geographic anomaly divergence, entropy deviation, and signal intensity thresholds. The model simulated swarm behavior across temporal CMIP grids, effectively capturing changes in climatic feedback and highlighting areas of ecological instability. The swarm framework dynamically analyzes pattern-based fluctuations in model output, facilitating ecocritical evaluation of environmental risk. This hybrid method integrates physically based climate data with adaptive artificial intelligence (AI) modeling, providing an ecologically contextual understanding of earth system changes and improving predictive insights for sustainability and policy formulation.
Crop prediction in Tamil Nadu according to environmental and soil factors using hybrid machine learning architecture Kannan Susee, Sundaraj; Shenbaga Vadivu, Shenbagaramasubramanian; Senthil Kumar, Murugesan
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp405-415

Abstract

Mathuranthagam, Tamil Nadu, India is the site of this research initiative that employs state-of-the-art hybrid machine learning (ML) architectures to forecast crop suitability in relation to environmental and soil characteristics. The model takes advantage of the strengths of linear support vector machine (SVM) classifier, bidirectional long short-term memory (BiLSTM), and convolutional LSTM (ConvLSTM) networks, and the data to capture complicated temporal and spatial correlations. To prepare the dataset for model training, it is normalized using min-max scaling and then feature selected using a Jaya optimization technique. The dataset contains variables such as humidity, rainfall, temperature, and pH. Both the BiLSTM and the ConvLSTM improve the model's comprehension of context from both previous and subsequent time steps. The ConvLSTM also records spatial dependencies. A powerful decision-making tool for differentiating across crop varieties is the linear SVM classifier. Comparing the hybrid model's performance to that of traditional LSTM approaches using measures such as recall, accuracy, precision, and F1-score shows that it performs much better. Using this approach can see how deep learning (DL) can supplement more conventional ML methods and see how important local environmental data is for agricultural policy and planning.
Effect of binder variations on the performance of one-phase induction motors in bio-pellet production process Ediwan, Ediwan; Hasibuan, Arnawan; Dabet, Abubakar; Daud, Muhammad; Lukman, Fajar Syahbakti; Supriadi, Gandi
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp253-260

Abstract

Indonesia has many oil palm plantation areas. One of the negative impacts is the large amount of empty fruit bunch (EFB) waste. Utilizing EFB as a bio pellet as a renewable energy source is one of the solutions to reduce waste while supporting the green energy transition. EFB bio-pellets have the potential to replace fossil fuels, but face challenges in setting good quality standards. The production process of EFB bio-pellets uses a variety of binder contents. This study aims to analyze the influence of different levels of binder content on the quality of bio-pellet products. Statistical analysis of linear regression was performed to measure energy consumption and motor performance in the production process of EFB bio-pellets. This study provides recommendations to help maximize the quality and efficiency of the bio-pellet production process from palm oil EFB waste.
State evolution approach for the axion conversion probability in magnetosphere of a neutron star Ahmad, Bilal; Ali, Shehreyar
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp355-371

Abstract

Neutron stars (NS), with their extreme gravitational and magnetic fields, provide an exceptional astrophysical laboratory for studying axion dark matter (DM). Through the Primakoff effect, axions can convert into photons within the magnetospheres of NS, a process that may produce observable radio and X-ray signals. In this work, we investigate axion-photon conversion using a novel, time-dependent state evolution formalism, moving beyond the commonly used stationary-path approximations. We derive a generic analytical expression for the conversion probability and calculate the associated radiated power. Our analysis demonstrates that this approach allows NS to strongly constrain the axion-photon coupling constant, reaching sensitivities of gaγγ ≃ 10−14 −10−15 GeV−1 for axion masses of ma ≃ 10−3 −10−10 eV. These results establish a new pathway to constrain gaγ via NS observations. Future campaigns using powerful observatories like the James Webb Space Telescope (JWST), Green Bank Telescope (GBT), and More Karoo Array Telescope (MeerKAT) array will be ideally suited to probe the distinct spectral signatures predicted by our model across multiple frequency domains.
Google Play review analysis on Sharia pawnshop applications in Indonesia Alam, Azhar; Kurniawan, Adityo Wiwit; Sholahuddin, Muhammad
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp86-98

Abstract

Digital transformation opens opportunities for Sharia pawnshops to develop innovative application-based services following Sharia principles. This study analyzes the perception and experience of users of the Sharia pawnshop application on the Google Play Store using a netnography approach. It collects and analyzes 395 user reviews between June and December 2024, which consist of 219 positive reviews and 176 negative reviews. The analysis shows that 59.82% of users gave positive reviews regarding satisfaction with using the application, especially regarding transaction security and ease of use. As many as 17.35% of positive reviews emphasized the benefits of the application in transforming Islamic financial services. The main challenges identified included update system problems (35.23%), technical and server problems (30.11%), and registration complexity (5.68%). There was also a discrepancy between numerical ratings and review content. Important concerns include service problems (9.66%) and limited choice of Islamic banks (5.11%). This research provides important insights for the development of digital Islamic finance applications in the future, especially in the aspects of improving technological infrastructure, simplifying processes, and improving the quality of customer service. The results of this study contribute to a better understanding of user needs in the context of the digitization of Islamic financial services in Indonesia.
Hydrothermal synthesis of ZnFe2O4@g-C3N4 for enhanced adsorption-photocatalytic degradation of ciprofloxacin Fitri, Medya Ayunda; Tamyiz, Muchammad; Kuncoro, Eko Prasetyo; Nihaya, Mamlu’atul; Thom, Muhammad Abdul Basith Thom; Cahyani, Cindy Dwi; Alqostolani, Bahauddin
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp313-321

Abstract

The persistence of antibiotic contaminants such as ciprofloxacin (CIP) in aquatic environments poses significant environmental and health risks, necessitating the development of efficient removal strategies. In this work, a zinc ferrite-anchored two-dimensional carbon nitride nanocomposite (ZF@2DCN) was synthesized via a simple calcination and hydrothermal approach to achieve synergistic adsorption–photocatalytic degradation of CIP under visible light. Structural and optical characterizations confirmed the successful formation of a ZF–2DCN heterojunction with high crystallinity, strong interfacial interactions, and enhanced visible-light absorption. The incorporation of ZF reduced the bandgap of 2DCN from 2.8 to 2.6 eV, promoting improved charge separation. Adsorption studies revealed rapid equilibrium within 30 min and multilayer adsorption on heterogeneous active sites, with a maximum adsorption capacity of 11.7 mg g-1. Under visible-light irradiation, ZF@2DCN achieved up to 81% CIP degradation within 60 min, exhibiting an apparent reaction rate approximately 2.5 times higher than that of pristine 2DCN. The enhanced performance is attributed to the strong synergy between adsorption-driven pollutant enrichment and photocatalytic degradation. Overall, ZF@2DCN shows strong potential as an efficient material for antibiotic removal in wastewater treatment.
Enhancing sleep disorder diagnosis through ensemble ML models: a comprehensive study on insomnia and sleep apnea Swain, Satyaprakash; Pattanayak, Binod Kumar; Mohanty, Mihir Narayan; Sahoo, Amiya Kumar; Jayasingh, Suvendra Kumar
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp29-41

Abstract

Sleep disorders are common and can significantly harm human health, with insomnia and sleep apnea being the most prevalent conditions. These disorders are often difficult to detect and treat accurately. Although machine learning (ML) techniques have shown promise in improving diagnostic precision and personalized treatment, most existing studies rely on single source data or conventional ML models, which limit their robustness and generalizability across diverse populations. To address this research gap, this study integrates multi-modal data and ensemble learning techniques to enhance accuracy, interpretability, and real-time applicability in diagnosing insomnia and sleep apnea. A dataset of 400 samples was collected through manual methods and internet of things (IoT) devices from multiple sources. Statistical techniques were applied for data cleaning, followed by principal component analysis (PCA) to reduce dimensionality and improve training efficiency. Four base ML models: decision tree (DT), support vector machine (SVM), naive Bayes (NB), and random forest (RF) were initially trained and evaluated. Subsequently, a boosting-based ensemble model was implemented to further improve performance. The proposed gradient boosting model with RF as the base learner achieved the highest diagnostic accuracy of 96.01%. The results demonstrate that ensemble ML models combined with multi-modal data significantly enhance the accuracy of insomnia and sleep apnea diagnosis.
Hybrid energy storage systems as a sustainable energy source Adam, Muhammad; Suwarno, Suwarno; Indra Cahyadi, Catra
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp219-226

Abstract

The use of fossil fuel power plants will contribute to emissions and environmental pollution, which has an impact on air and environmental pollution. Applying hybrid energy systems can help reduce the emission footprint and improve the stability of local electricity networks, especially in services with high energy consumption. Hybrid optimization of multiple energy resources (HOMER) is a simulator that simulates using renewable energy with the hybrid renewable energy systems (HRES). The simulation produces a system with the most appropriate combination of photovoltaic (PV), wind power (WP), and converter. The combination of PV-WP produces an economical choice for providing electrical energy in a particular location. The hybrid PV-WP model can save about 40.8% less than the current condition. The investment can be returned in 10.11 years, which is recommended for similar conditions in other areas. This positive impact can provide incentives for policymakers in the implementation of a hybrid system that can neutralize emissions and environmental pollution.
Ensemble machine learning based model to estimate irrigation water requirement for wheat crop Jain, Satendra Kumar; Kumar Gupta, Anil
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp142-154

Abstract

India faces a serious water shortage issue, as its population grows faster than the percentage of fresh water available, with only 4% of the world's fresh water available to 18% of the world's population. Agriculture sector is more water-consuming sector in India. India's irrigation system still faces two significant problems: low irrigation efficiency and a lack of optimization during irrigation. To address these problems, agriculturists ought to be aware of the water requirements for crops beforehand. Innovative fields like machine learning, a branch of artificial intelligence, have a big potential to improve irrigation. Verifying the suitability of the gradient boosting regressor machine learning algorithm-based model for estimating irrigation water requirements (IWR) is the aim of this research. The experiment is conducted in Ludhiana, a city in Central Punjab, India, with a hot, semi-arid climate that features scorching summers and chilly winters. The results demonstrate the remarkably high accuracy rate with coefficient of determination (R2) =0.98 for predicting IWR. The suggested model, which is based on a gradient boosting regression, allows the stakeholders to accurately estimate the amount of water needed for irrigation, the number of irrigation applications for the growing season of wheat crops, and the interval between irrigations.
Soft fuzzy partial metric and some results on fixed point theory under soft set Gore, Rohini R.; Pathak, Renu P.
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp427-436

Abstract

This research paper establishes a new concept of soft fuzzy partial metric spaces, combining soft sets, partial metric spaces, and fuzzy sets to handle uncertainty and imprecision. This paper's primary goal is to use soft fuzzy partial metric spaces to examine various fixed-point theory conclusions. A few fixed-point results are defined under the ? −contraction mapping on soft fuzzy partial metric space and the soft fuzzy contraction mapping. Also, illustrate the related example of fixed-point theorem. Soft fuzzy partial metric spaces have applications in various fields, including image processing, decision-making analysis.

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