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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 84 Documents
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Search results for , issue "Vol 14, No 4: August 2025" : 84 Documents clear
A fusion convolution neural network-local binary pattern histogram algorithm for emotion recognition in human Katti, Arpana Giridhar; Melekote Vinayakamurthy, Chidananda Murthy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2734-2740

Abstract

This paper proposes a fusion of algorithms namely convolution neural networks (CNN) and local binary pattern histogram (LBPH) techniques to comprehend the emotions in humans for greyscale images. In this work, the combined advantages of CNN for its ability to extract features, suitability for image processing and LBPH algorithm to identify the emotions of the human images are included. Though there are enhanced fused algorithms with CNN for image processing, the combination of LBPH with CNN is precise and simple in design. In this work, the secondary data sample is used to recognize the human emotions. The secondary data set consists of 160 samples with emotions of happy, anger, sad, and surprise is considered for making decisions. In comparison, the accuracy of the proposed method is high compared to the other algorithms.
Hybrid forecasting methods across varied domains-a systematic review Xhabafti, Malvina; Sinaj, Valentina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2601-2612

Abstract

Time series forecasting is one of the links that has developed since early times due to risk management, efficient allocation of resources, performance evaluation, strategic planning, and the formulation of effective policies for individuals, organizations, and societies. Forecasting models have evolved steadily by hybridizing statistical and neural network techniques ensuring efficiency and accurate predictions. In this paper, a systematic review of the literature was made through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, highlighting the domains that mostly use hybrid techniques by defining the ones with the highest frequency of implementation in each domain we predefined. During the selection process from the 4 selected databases, 2251 works were taken into consideration, of which 25 were the ones that were included in the review process through various filtering steps and exclusion criteria. Ongoing, we defined four main categories where we presented each paper individually by briefly explaining the underlying data, the proposed hybrid forecasting approach and the evaluation performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a summary table, we highlight the most used hybrid methods for each domain, concluding which of the statistical and deep learning methods are mostly applied in the specified domains.
Fine-tuning bidirectional encoder representations from transformers for the X social media personality detection Khoerunnisa, Selvi Fitria; Surarso, Bayu; Kusumaningrum, Retno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3395-3403

Abstract

Understanding personality traits can help individuals reach their full potential and has applications in various fields such as recruitment, advertising, and marketing. A widely used tool for assessing personality is Myers-Briggs type indicator (MBTI). Recent advancements in technology have allowed for research on how personalities can change based on social media use. Previous research used machine learning methods, deep learning methods, until transformers-based method. However, these previous approaches must be revised to require extensive data and a high computational load. Although transformer-based methods like bidirectional encoder representations from transformers (BERT) excel at understanding context, it still has limitations in capturing word order and stylistic variations. Therefore, this study proposed integrating fine-tuning BERT with recurrent neural networks (RNNs) consisting of vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU). This study also uses a BERT base fully connected layer as a comparison. The results show that the BERT base fully connected layer approach strategy has the best evaluation results in class extraversion/introversion (E/I) of 0.562 and class feeling/thinking (F/T) of 0.538. then, the BERT+LSTM approach strategy has the highest accuracy for the intuition/sensing (N/S) class of 0.543 and judging/perceiving (J/P) of 0.532. 
Machine learning application for particle accelerator optimization-a review Rachmawati, Isti Dian; Effendy, Nazrul; Taufik, Taufik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3014-3021

Abstract

Particle accelerators receive significant attention from researchers. This machine consists of various interdependent elements, so it is complex. Efficient system tuning and diagnostics are essential for utilizing accelerator technology. In addition, machine learning (ML) has been applied in several applications. ML methods such as artificial neural networks, random forest, reinforcement learning, genetic algorithm, and Bayesian optimization have been used for accelerator optimization. The optimization of particle accelerators covers their performance and efficiency. This paper reviews the application of ML techniques in optimizing particle accelerators, highlighting their importance in addressing the complexity inherent in accelerator systems and advancing accelerator science and technology.
Child-friendly e-learning for artificial intelligence education in Indonesia: conceptual design Purbohadi, Dwijoko; Santoso, Joko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2622-2633

Abstract

Due to the widespread use of smartphones, most children in Indonesia are now engaged in playing video games. To make these games more exciting and challenging, video game manufacturers often incorporate artificial intelligence (AI). While various studies have highlighted the benefits of playing video games for children, this research has revealed some significant negative impacts that need to be addressed, as they can affect children's prospects. One of the major detrimental effects is the growing negative perception towards robots and AI, with concerns that they will replace human jobs. To counteract these negative impacts, educational institutions in Indonesia need to proactively plan and prepare for the consequences of gaming through formal learning. Given Indonesia's vast territory, consisting of islands, and its large population, it is crucial to implement appropriate learning technology. This article presents the architectural design of a child-friendly e-learning system that focuses on teaching children about AI. The design considers the available technology in Indonesia, based on our experience. The child-friendly e-learning model for AI education is expected to cultivate an interest in learning about technology, thus diverting children's attention from video game addiction.
Optimizing traffic lights at unbalanced intersections using deep reinforcement learning Khrisne, Duman Care; Sudarma, Made; Giriantari, Ida Ayu Dwi; Wiharta, Dewa Made
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2991-3002

Abstract

Unbalanced intersectional traffic flow increases vehicle delays, fuel consumption, and pollution. This study investigates the application of deep reinforcement learning (DRL) to optimize traffic signal timing at the Pamelisan intersection in Denpasar, Indonesia. Real-world traffic data were incorporated into a SUMO microsimulation environment to train DRL agents using the deep Q-network (DQN) algorithm. Experimental results show that DRL-based optimization reduced the average vehicle waiting time from 594.49 seconds (static control) to 169.44 seconds and 173.10 seconds for agents trained without and with noise, respectively. The average vehicle speed remained stable at 5.6–5.97 m/s across all scenarios, indicating enhanced traffic efficiency without adverse effects. The findings underscore the effectiveness and adaptability of DRL in addressing traffic inefficiencies, optimizing them, and offering a robust solution for dynamic traffic management at unbalanced traffic intersections in urban areas.
Classification of Kannada documents using novel semantic symbolic representation and selection method Rangan, Ranganathbabu Kasturi; Harish, Bukahally Somashekar; Roopa, Chaluvegowda Kanakalakshmi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3354-3365

Abstract

Kannada is one of the 22 scheduled Indian regional languages. It is also a low-resource regional language. The Kannada document classification is arduous due to its vocabulary richness, agglutinative terms, and lack of resources. The good representation and the prominent feature selection aid in solving the challenges in document classification tasks. In this paper, we are proposing semantic symbolic representation and feature selection method, for better representation of Kannada terms in interval values embedded with positional information. Following, selection of prominent discriminative symbolic feature vectors is also proposed. Further the symbolic document classifier is used to classify the Kannada documents. The proposed cluster based symbolic representation preserves the intra class variance and reduces the ambiguity in classification of Kannada documents. The experiments are performed over two Kannada document datasets which are multilabel and unbalanced. The comparative analysis of proposed method with other standard methods is also presented.
Myoelectric grip force prediction using deep learning for hand robot Anam, Khairul; Ardhiansyah, Dheny Dwi; Hana Sasono, Muchamad Arif; Nanda Imron, Arizal Mujibtamala; Rizal, Naufal Ainur; Ramadhan, Mochamad Edoward; Muttaqin, Aris Zainul; Castellini, Claudio; Sumardi, Sumardi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3228-3240

Abstract

Artificial intelligence (AI) has been widely applied in the medical world. One such application is a hand-driven robot based on user intention prediction. The purpose of this research is to control the grip strength of a robot based on the user’s intention by predicting the grip strength of the user using deep learning and electromyographic signals. The grip strength of the target hand is obtained from a handgrip dynamometer paired with electromyographic signals as training data. We evaluated a convolutional neural network (CNN) with two different architectures. The input to CNN was the root mean square (RMS) and mean absolute value (MAV). The grip strength of the hand dynamometer was used as a reference value for a low-level controller for the robotic hand. The experimental results show that CNN succeeded in predicting hand grip strength and controlling grip strength with a root mean square error (RMSE) of 2.35 N using the RMS feature. A comparison with a state-of-the-art regression method also shows that a CNN can better predict the grip strength.
Generative Indonesian chatbot for university major selection using transformers embedding Khadija, Mutiara Auliya; Harjito, Bambang; Saberi, Morteza; Paradhita, Astrid Noviana; Nurharjadmo, Wahyu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3474-3482

Abstract

Selecting a university major is a crucial decision that impacts students' future career paths and personal fulfillment. Traditional guidance methods often lack the personalization and timeliness needed to support students effectively. This study explores the use of Indonesian generative artificial intelligence (AI) chatbots and transformer embeddings to enhance decision-making for university major selection. By leveraging advanced AI techniques, such as bidirectional encoder representations from transformers (BERT) and Gemini embeddings, the research aims to provide personalized, interactive, and contextually relevant guidance. Experiments showed that BERT embeddings achieved the highest accuracy, with recurrent neural network (RNN) and long short-term memory (LSTM) models also performing well but facing issues with overfitting. Gemini embeddings provided strong performance but slightly less effective than BERT. The findings suggest that BERT-based models with RNN are superior for developing decision-support systems in 92% accuracy. Future work should focus on further optimization and integration of user feedback to ensure the relevance and effectiveness of these AI tools in educational settings.
Synthesizing strategies and innovations in combating land degradation: a global perspective on sustainability and resilience Hediyalad, Gangamma; Kukkuvada, Ashoka; Hegde Kota, Govardhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3133-3142

Abstract

This paper presents a comprehensive examination of land degradation, a critical environmental challenge with far-reaching implications for agricultural productivity, ecosystem sustainability, and socio-economic stability worldwide. With the backdrop of escalating human population pressures and the exacerbating impact of climate change. It delves into the causes and consequences of soil erosion, desertification, salinization, and biodiversity loss, highlighting the interplay between natural processes and anthropogenic activities. Through a detailed review of literature spanning various remediation technologies, conservation practices, and policy frameworks, the paper critically assesses the effectiveness of current land management approaches, including the utilization of biosurfactants, remote sensing technologies, and agroforestry systems. Furthermore, it identifies significant research gaps and future directions, emphasizing the need for quantitative assessments, exploration of socio-economic impacts, and evaluation of restoration techniques. By offering evidence-based recommendations for policymakers and practitioners, this paper contributes to the global dialogue on sustainable land management and aims to catalyze action towards halting the advance of land degradation, ensuring food security, and preserving biodiversity for future generations. This work not only advances our understanding of land degradation challenges but also outlines a path forward for research, policy, and practice in the pursuit of environmental sustainability and resilience.

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