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INDONESIA
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 683 Documents
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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 fasteners 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.

Page 69 of 69 | Total Record : 683

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