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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.
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Articles 83 Documents
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Search results for , issue "Vol 14, No 5: October 2025" : 83 Documents clear
Enhancing energy efficiency and accuracy in IoT-based wireless sensor networks using machine learning Shankar Sollapure, Naganna; Govindaswamy, Poornima
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3869-3878

Abstract

This study presents a novel sensor data fusion framework designed to improve accuracy and energy efficiency in internet of things (IoT)-driven wireless sensor networks (WSNs). The proposed approach combines machine learning techniques with the Kalman filter, addressing the limitations of traditional methods, such as high computational overhead and limited precision. By utilizing machine learning algorithms for pattern recognition and the Kalman filter for precise state estimation, the framework optimizes data processing while minimizing energy consumption. MATLAB-based simulations validate the model’s effectiveness, demonstrating a significant improvement in key performance metrics, including F1-score, recall, and precision, with an overall accuracy of 98.36%. The results highlight the framework’s ability to enhance fault tolerance, accelerate convergence rates, extend network lifespan, and optimize energy utilization, making it highly suitable for real-time data fusion applications in complex sensor environments. Furthermore, the proposed hybrid model is scalable and adaptable, allowing it to be implemented across various fields, including environmental surveillance, industrial automation, and healthcare monitoring. With integration of intelligent data processing techniques, this research contributes to the development of sustainable and efficient IoT-based monitoring systems capable of handling dynamic and resource-constrained environments.
An improved real time detection transformer method for retail product detection Wahyu Maulana, Andi; Adhi Wibowo, Suryo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4090-4100

Abstract

The main problem in retail product detection is intra-class variation, as some products have similar but distinct characteristics. The primary goal of this study is to address the problem of object detection on intra-class variation in retail environments. As a result, a new approach for object detection of retail products was developed by modifying the Real Time Detection Transformer (RT-DETR) model. To manage intra-class variation more successfully, the RT-DETR model is updated by modifying its architecture. There are two convolutions in the Contextual Cross-Feature Module (CCFM) fusion block section, which is adjusted by adding one convolution layer to each CCFM fusion block. A customized dataset was meticulously constructed to reflect the wide range of products frequently seen in retail outlets. For the constructed datasets, tests were run using the mean Average Precision (mAP) metric, which had a mAP0.5 of 99.5% and a mAP0.5:0.95 of 88.2%. The updated model is superior compared to original model. The difference in mAP0.5:0.95 was 2.5%, while precision increased by 1.3% and recall increased by 0.1%. Although the mAP0.5 results stay unchanged, the gains in the other metrics suggest that the RT-DETR model modifications can improve object detection skills, particularly when dealing with intra-class variation in retail merchandise.
Facial features extraction using active shape model and constrained local model: a comprehensive analysis study Iqtait, Musab; Alqaryouti, Marwan Harb; Sadeq, Ala Eddin; Abuowaida, Suhaila; Issa, Abedalhakeem; Almatarneh, Sattam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4299-4307

Abstract

Human facial feature extraction plays a critical role in various applications, including biorobotics, polygraph testing, and driver fatigue monitoring. However, many existing algorithms rely on end-to-end models that construct complex classifiers directly from face images, leading to poor interpretability. Additionally, these models often fail to capture dynamic information effectively due to insufficient consideration of respondents' personal characteristics. To address these limitations, this paper evaluates two prominent approaches: the constrained local model (CLM), which accurately extracts facial features depending on patch experts, and the active shape model (ASM), designed to simultaneously extract the appearance and shape of an object. We assess the performance of these models on the MORPH dataset using point to point error as evaluation metrics. Our experimental results demonstrate that the CLM achieves higher accuracy, while the ASM exhibits better efficiency. These findings provide valuable insights for selecting the appropriate model based on specific application requirements.
Multilabel classification sentiment analysis on Indonesian mobile app reviews Riccosan, Riccosan; Saputra, Karen Etania
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4226-4234

Abstract

Mobile applications continue to evolve to satisfy the users. For that, the developers need to understand user feedback for improvements. Indonesia, one of the countries with the most mobile app users, has many textual mobile app reviews that may be processed and analyzed. Understanding the value of mobile app reviews requires understanding the value of sentiments and emotions to create more appropriate features to satisfy the users. To acquire a more accurate analysis of user reviews, it is important to detect sentiments that are closely associated with human emotion values due to the nature of multilabeled data. This research classifies the sentiments and emotions in Indonesian textual mobile app reviews, which are multilabel and multiclass in the form of 3 sentiments, namely positive, negative, and neutral, paired with 6 emotions, namely anger, sad, fear, happy, love, and neutral. We employ the Transformers architecture model, which includes two monolingual (a generic English and an Indonesian) and a multilingual pre-trained models with the results: bidirectional encoder representations from transformers (BERT) base uncased (micro avg. F1-score=0.69, precision=0.68, recall=0.70, receiver operating characteristic-area under the curve (ROC-AUC)=0.78), IndoBERT base uncased as best result (micro avg. F1-score=0.77, precision=0.78, recall=0.76, ROC-AUC=0.85), and multilingual BERT (M-BERT) base uncased (micro avg. F1-score=0.72, precision=0.73, recall=0.71, ROC-AUC=0.82).
Intelligent assessment of harmonic distortion compliance in reverse osmosis systems Lahlou, Cherki; Bouikhalene, Belaid; Bengourram, Jamaa; Latrache, Hassan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4371-4381

Abstract

This study explores the critical challenge of harmonic distortion compliance in reverse osmosis (RO) desalination systems, with a focus on aligning with international standards, specifically IEC 61000, IEEE 519, and EN 50160. High-power equipment, particularly high-pressure pumps (HPP), introduces significant harmonic distortions, threatening power quality and operational reliability. To address this issue, we integrated advanced machine learning (ML) algorithms, namely decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) to assess harmonic compliance and predict total harmonic distortion (THD) under four operational scenarios. All data used for training and testing were obtained from real-time measurements taken at a large-scale desalination plant using a power quality analyzer (QUALISTAR CA 8336), which guarantees the practical relevance of the analysis. The models were trained on harmonic order and amplitude data and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models, MLP demonstrated superior performance, achieving an accuracy of 99.11% and an F1-score of 98.9%, making it a robust tool for harmonic compliance assessment. SVM and RF also showed commendable results, while DT proved effective for basic analysis. This research underscores the potential of AI-driven approaches in mitigating harmonic-related challenges, optimizing power quality, and enhancing operational efficiency in RO plants. These findings offer a pathway toward more reliable and energy-efficient industrial operations.
Automated menu planning for pregnancy based on nutrition and budget using population-based optimization method Kurnianingtyas, Diva; Daud, Nathan; Arai, Kohei; Indriati, Indriati; Marji, Marji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3483-3492

Abstract

Nutritional fulfilment during pregnancy depends on the budget. Meanwhile, nutrition is needed during pregnancy to keep the mother and fetus healthy. Therefore, this study aims to assist maternal nutrition planning by using population-based optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), duck swarm algorithm (DSA), and whale optimization (WO) according to their nutritional needs at minimum cost. Additionally, this study compares the method performance to find the best method. There are 55 foods obtained from previous studies divided into five groups: staple food (SF), vegetables (VG), plant-source food (PS), animal-source food (AS), and complementary (CP). The model evaluation results show that GA's performance differed significantly from other models because it obtained the highest fitness by 439.73 and more variation in fitness results. Three models other than GA have no significant difference, but DSA performance obtained a superior fitness of 367.18. Furthermore, optimization methods must be combined with other artificial intelligence methods to develop innovative technology to support maternal nutrition and prevent stunting.
IoT-enabled Edge Impulse approach for heat stress prediction in outdoor settings Ke Yin, Lim; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Sayeed, Md. Shohel; Bukar, Umar Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3934-3944

Abstract

Several international organizations of public health or countries have predicted the rise of heat-related illness cases due to climate change, which result high environment temperature. Previous studies of heat-related illness prediction using internet of things (IoT) and machine learning (ML) are mainly focusing on early detection or prediction of heat stroke incidence. To overcome the problem of heat stress prediction in outdoor settings, especially for an individual, the objective of this study is to identify a prediction method for heat stress using IoT technology and analyze the accuracy of the identified prediction model. Arduino nano 33 BLE sense with Bluetooth low energy (BLE) connectivity, HTS221 embedded environment temperature and humidity sensor, MLX90614 skin temperature sensor, and MAX30100 heart rate sensor were used to build IoT based wearable device. Besides, Python language is used for data pre-processing and data labelling after getting the sensor data from wearable device. Lastly, model training using neural network algorithms was directed in Edge Impulse. The result shows 94.6% of training accuracy with the loss of 0.27 and 90.22% of accuracy in testing set.
An energy-efficient and secure framework for wireless sensor networks Chandrappa, Maruthi Hanumanthappa; Govindaswamy, Poornima
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4151-4161

Abstract

In wireless sensor networks (WSNs), achieving energy efficiency, security, and minimizing route change propagation time is essential for maintaining optimal performance. This paper introduces a new approach that combines Bray Jaccard Curtis-based Calinski Harabasz k-means (BJC-CHKMeans) for clustering and Karl Pearson correlation-based egret swarm optimization algorithm (KPC-ESOA) for selecting the best cluster head (CH) and path, along with classifying long short-term memory with gated recurrent units (CLE-GRU) for detecting harmful nodes. The methodology aims to enhance energy usage, improve routing efficiency, and strengthen security by identifying malicious nodes. Additionally, it integrates a secure routing table using elbow de-swinging k-anonymity (EDS-KA) and employs digital signature algorithm-based Zeta Bernoulli Merkle tree (DSA-ZBMT) to ensure secure communication with sink nodes. The WSN-DS dataset was used for training and testing, with rigorous preprocessing, feature extraction, and selection to maintain data integrity. Experimental results revealed that the proposed BJC-CHKMeans and CLE-GRU models outperform traditional methods in power consumption, latency, and accuracy. The system achieved a power consumption of 2.1 mW for clustering and 1.9 mW for classification, while also providing near-perfect accuracy in detecting harmful nodes. These findings demonstrate that the framework significantly enhances the energy efficiency and security of WSNs, making it a highly effective solution for large, dynamic sensor networks.
Facial paralysis image analysis for stroke detection using deep ensemble transfer learning and optimization Subramaniyan, Kiruthiga; Anbuananth, Chinnasamy; Venkatesan, Dhilip Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4074-4089

Abstract

Facial paralysis (FP) weakens facial muscles, leading to asymmetric facial actions and complicating stroke diagnosis. Machine learning (ML) and deep learning (DL) systems have been explored for diagnosing FP, but the effectiveness of these methods is hindered by the limited size and diversity of available datasets. This study proposes a novel deep ensemble transfer learning method for accurate stroke diagnosis using facial paralysis imaging (DETLM-ASDFPI). The method leverages pre-trained models to reduce computation costs on edge devices. The framework includes data acquisition, preparation, and pre-processing, with image rescaling to standardize input dimensions. Feature extraction is performed using a deep capsule network (DCapsNet) to capture complex features. For stroke detection, an ensemble transfer learning model integrates three classifiers: gated recurrent unit (GRU), deep convolutional neural network (DCNN), and stacked sparse auto-encoder (SSAE). The hippopotamus optimization algorithm (HOA) is applied to fine-tune model parameters. The method was validated using two benchmark datasets, Massachusetts eye and ear infirmary (MEEI) and YouTube facial palsy (YFP), achieving an accuracy of 97.06%, outperforming recent approaches. This research demonstrates the effectiveness of the DETLM-ASDFPI method in accurately diagnosing strokes from FP images while addressing challenges related to dataset limitations and computational efficiency.
Intent detection in AI chatbots: a comprehensive review of techniques and the role of external knowledge K., Jemimah; Kannan, Rajkumar; Andres, Frederic
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4250-4259

Abstract

Artificial intelligence (AI) chatbots have become essential across various industries, including customer service, healthcare, education, and entertainment, enabling seamless, and intelligent user interactions. A key component of chatbot functionality is intent detection, which determines the underlying purpose of user queries to provide relevant responses. Traditional intent detection methods, such as rule-based and statistical approaches, often struggle with adaptability, especially in complex, dynamic conversations. This review examines the evolution of intent detection techniques, from early methods to modern deep learning and knowledge-enriched models. It introduces the domain type-conversation turns-adaptivity-external knowledge (DCAD) classification, highlighting its significance in improving chatbot accuracy and contextual awareness. The paper categorizes existing intent detection models, analyzes their applications across various sectors, and discusses key challenges, including data integration, language ambiguity, and ethical concerns. By exploring emerging trends and future directions, this review underscores the critical role of external knowledge in enhancing chatbot performance and user experience.

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