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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 81 Documents
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Search results for , issue "Vol 14, No 1: February 2025" : 81 Documents clear
Development of a 2 degree of freedom-proportional integral derivative controller using the hippopotamus algorithm Dulyala, Rattapon; Sa-ngiamvibool, Worawat; Audomsi, Sitthisak; Ardhah, Kittipong; Buranaaudsawakul, Techatat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp780-787

Abstract

This research project investigates the regulation of autonomous power generation in two interconnected regions using two hydroelectric power plants. It specifically addresses the challenges posed by significant electrical system issues. The hippopotamus optimization algorithm (HOA) has demonstrated enhanced gain value in research and designs of 2 degree of freedom (2DOF)-proportional integral derivative (PID) controllers. The objective is to provide efficient and uninterrupted functioning of the electrical network in both areas. Contemporary technology and methods enable the electrical system to efficiently and accurately fulfill user requirements, resolving any problems related to system balance and stability. This experiment evaluates the efficacy of several algorithms in accurately selecting optimal values. We evaluate performance using the integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE) functions. This experiment evaluates and contrasts different algorithms. Summarizing the analysis using verifiable evidence. Optimization when evaluated using the ITAE measurement, the HOA earned the lowest result of 0.08744 for ITAE. Empirical research has demonstrated that this strategy is the most effective in reducing the ITAE. The sine-cosine algorithm (SCA) and whale optimization algorithm (WOA) have similar ITAE values, with SCA having an error of 0.08967 and WOA having an error of 0.08967. The numerical number is 0.08970.
Detecting human fall using internet of things devices for healthcare applications Benhaili, Zakaria; Balouki, Youssef; Moumoun, Lahcen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp561-569

Abstract

Falls pose a significant threat to unintentional injuries, particularly impacting the independence of older individuals. Existing detection methods suffer from drawbacks, including inaccuracies, wearer discomfort, complex setup, resource-intensive computation, and limitations in detecting falls outside a specific setting. In response, our innovative fall detection system integrates with a pneumatic solution, analyzing fundamental human activities like running, walking, and sitting, both indoors and outdoors. This approach combines wearable sensors with a vision-based solution, utilizing a smart belt with embedded accelerometer and gyroscope, alongside wall-installed cameras in a smart house. The system triggers an airbag and sends an emergency alarm upon fall detection. To achieve this, we propose FallMixer a lightweight deep learning model, combined with ‘you only look once’ version 8 (YOLOv8) algorithm, fine-tuned on a collected video dataset to enable real-time detection. We found that the models result in competitive performance, as demonstrated on SisFall, UCI human activity recognition (HAR), and mobile health (MHEALTH) datasets with a remarkable mean average precision. Subsequently, we assess the hardware performance of our solution on edge devices.
Bias in artificial intelligence: smart solutions for detection, mitigation, and ethical strategies in real-world applications Samala, Agariadne Dwinggo; Rawas, Soha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp32-43

Abstract

Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.
An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources Shankara, Kavitha Hosakote; Hosakote Shankara, Kavitha; Srikantaswamy, Mallikarjunaswamy; Nagaraju, Sharmila
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp307-316

Abstract

This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management.
Unmanned aircraft vehicles/unmanned aerial systems digital twinning: Data-driven lift and drag prediction for airfoil design Aghazadeh Ardebili, Ali; Martella, Angelo; Khalil, Adem; Khalil, Sabri; Longo, Antonella; Ficarella, Antonio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp240-251

Abstract

This study investigates the innovative application of neural networks algorithms in the aviation industry's mechanical design process, motivated by the pursuit of creating a more accurate and efficient method for performance prediction. Traditional approaches, such as computational fluid dynamics (CFD) simulations based on solving Navier-Stokes’s equations, demand substantial computational power and often exhibit limited accuracy, particularly when compared with complex geometries. The state-of-the-art review unveils a growing research trend advocating for data-driven methodologies to revolutionize design practices, addressing the limitations of conventional techniques. The primary objective of this study is to explore how neural network algorithms can overcome the drawbacks of CFD simulations, offering a more effective alternative for predicting the performance of airfoils. To achieve this objective, we conducted a performance analysis of airfoils using neural network algorithms. The results presented a promising avenue for a more accurate and efficient performance prediction method through digital twinning. The study highlights the advantageous features of neural network methods in unmanned aircraft vehicles (UAV) component mechanical design, showcasing their potential to outperform traditional methods and offering practical recommendations for integration into the design process.
Predicting water resistance and pitching angle during take-off: an artificial neural network approach Fajar, Muhammad; Atmaja, Sigit Tri; Pinindriya, Sinung Tirtha; Soemaryanto, Arifin Rasyadi; Hidayat, Kurnia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp142-150

Abstract

This research addresses the challenges faced by seaplanes and amphibious aircraft during takeoff and landing on water, emphasizing the limitations and costs associated with traditional towing tank tests and computational fluid dynamics (CFD) simulations. The study proposes an innovative approach that employs artificial neural networks (ANN) to predict water resistance and pitching angle during amphibious aircraft take-off, minimizing the reliance on expensive towing tank tests. The ANN models are developed and optimized using Bayesian optimization, showcasing improved accuracy in predicting water resistance and pitching angle. The research demonstrates the potential of machine learning, specifically ANNs, to significantly reduce the need for costly experimental tests, providing an efficient alternative for designing amphibious aircraft. The results indicate high accuracy in predicting water resistance and pitching angle, offering substantial time and resource savings during the experimental phase. However, the study highlights the need for model adaptation for different designs and test variations to enhance overall applicability.
A revolutionary convolutional neural network architecture for more accurate lung cancer classification Muliadi, Muliadi; Windarto, Agus Perdana; Solikhun, Solikhun; Alkhairi, Putrama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp516-526

Abstract

This research aimed to investigate a breakthrough in convolutional neural network (CNN) architecture with the potential to revolutionize lung cancer classification. The proposed method is a comparative optimization model of ResNet architecture, with accuracy rate of 99.68% in identifying and categorizing lung cancer types. The results showed that the use of innovative methods in CNN architecture, such as multi-dimensional convolutional layers and the integration of specific lung cancer features, effectively provided highly accurate and reliable outcomes. These showed a positive impact on the development of medical diagnostic technology, offering promising potential to enhance prognosis and response to treatment for lung cancer patients. With high accuracy rate, this breakthrough presents opportunities for further advancements in lung cancer management through artificial intelligence-based methods.
Comparative analysis of genetic algorithms for automated test case generation to support software quality Hadiningrum, Tiara Rahmania; Rochimah, Siti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp252-259

Abstract

Software testing is crucial for enhancing software quality, but designing test cases is a labor-intensive, resource-intensive, and time-consuming process. Additionally, test case designers often introduce subjectivity when creating test cases manually. To address these challenges, this paper compares three different approaches for automatically generating program branch coverage test cases: the parallel data generation algorithm (PDGA), a standard genetic algorithm (SGA), and a random test generation method. By leveraging genetic algorithms and parallel data generation techniques, these automated approaches aim to reduce the manual effort, resources, and potential biases involved in test case design, while improving the efficiency and effectiveness of achieving comprehensive branch coverage during software testing. The experimental results, conducted using five datasets with programs written in PHP, demonstrate that PDGA outperforms both SGA and random methods across various tested programs, achieving higher maximum and average coverage. Specifically, PDGA achieved an average coverage of 100% in the "calculator" program, highlighting its superior stability and efficiency. While SGA also shows good performance, it is not as optimal as PDGA, and the random method shows the lowest performance among the three. These findings underscore the potential of genetic algorithms, particularly PDGA, to enhance the coverage and quality of software testing, thereby significantly improving system reliability. 
Discriminative deep learning based hybrid spectro-temporal features for synthetic voice spoofing detection Palsapure, Pranita Niraj; Rajeswari, Rajeswari; Kempegowda, Sandeep Kumar; Ravikumar, Kumbhar Trupti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp130-141

Abstract

Voice-based systems like speaker identification systems (SIS) and automatic speaker verification systems (ASV) are proliferating across industries such as finance and healthcare due to their utility in identity verification through unique speech pattern analysis. Despite their advancements, ASVs are susceptible to various spoofing attacks, including logical and replay attacks, posing challenges due to the sophisticated acoustic distinctions between authentic and spoofed voices. To counteract, this study proposes a robust yet computationally efficient countermeasure system, utilizing a systematic data processing pipeline coupled with a hybrid spectral-temporal learning approach. The aim is to identify effective features that optimize the model's detection accuracy and computational efficiency. The model achieved superior performance with an accuracy of 99.44% and an equal error rate (EER) of 0.014 in the logical access scenario of the ASVspoof 2019 challenge, demonstrating its enhanced accuracy and reliability in detecting spoofing attacks with minimized error margin. 
Comparison of faster region-based convolutional network for algorithms for grape leaves classification Sarosa, Moechammad; Ma'rifah, Puteri Nurul; Kusumawardani, Mila; Al Riza, Dimas Firmanda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp222-230

Abstract

The shapes of leaves distinguish the Indonesian grape variants. The grape leaves might look the same at first glance, but there are differences in leaf shapes and characteristics when observed closely. This research uses a deep learning method combined with the faster region-based convolutional neural network (R-CNN) algorithm with the Inception network architecture, ResNet V2, ResNet-152, ResNet-101, and ResNet-50, and uses COCO weights trained to classify five grape varieties through leaf images. The study collected 500 images to be used as an independent dataset. The results show that network improvements can effectively improve operating efficiency. There are also limitations to training scores because the F1 score value tends to stabilize or decrease at a certain point. In the Inception ResNet V2 architecture, with the highest average F1 score of 92%, the average computing time for training and testing is longer than other network architectures. This suggests that the algorithm can classify types of grapes based on their leaves.

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