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30,376 Article Results

Unveiling critical features for failure prediction in green internet of things applications

10.11591/ijai.v14.i5.pp4308-4318
Ouiam Khattach , Omar Moussaoui , Mohammed Hassine
The rapid growth of the green internet of things (GIoT) in recent years signifies a transformative shift in internet of things (IoT) solution development. This evolution is driven by technological advancements, heightened environmental awareness, and a global imperative to combat climate change. Ensuring the reliability of GIoT applications is crucial for their success. This study identifies critical features for predicting IoT device failures, enabling early detection and intervention. Using datasets from industry, energy, and agriculture sectors, we employ a feature selection strategy to analyze extensive data from diverse GIoT deployments. Our analysis identifies significant features and integrates key insights from existing literature. Our findings support enhanced predictive maintenance strategies, reduced downtime, and improved overall performance of sustainable IoT solutions.
Volume: 14
Issue: 5
Page: 4308-4318
Publish at: 2025-10-01

Temporal context of lightweight network model for detecting boats approaching the tsunami early warning system

10.11591/ijai.v14.i5.pp3542-3553
Wayan Wira Yogantara , Suprijanto Suprijanto , Anak Agung Ngurah Ananda Kusuma , Yuki Istianto
The tsunami early warning system (TEWS) is a device that detects potential tsunamis. However, a boat that approaches TEWS is a source of communication disturbance. A convolutional neural network (CNN), as part of intelligent computer vision, is one solution for detecting boats and providing a warning to move away from the TEWS area. Water segmentation and refinement-temporal (WaSR-T), as the current advanced CNN network, exhibits impressive performance in detecting object obstacles in the marine domain, although it requires a powerful computational device. In the paper, we propose a modification of WaSR-T, replacing the most computationally intensive stages with a lightweight version called lightweight WaSR-T. On the proposed lightweight WaSR-T, the previous encoder of WaSR-T was replaced with MobileNetV3, and some feature layer maps were reduced as input to the decoder. For training and validating the lightweight WaSR-T, the image dataset representing the open sea and our extended dataset from Indonesia's ocean region were used. Based on the quantitative results and evaluation of the computational load, the sensitivity to detect a boat for WaSR-T and lightweight WaSR-T is 95.71% and 90.00%, respectively. The lightweight WaSR-T required less memory at 32.57%, resulting in a 0.0761% reduction in total processing time compared to the original WaSR-T. Therefore, our proposed lightweight WaSR-T is promising for use as the central part of an intelligent maritime computer vision system in TEWS.
Volume: 14
Issue: 5
Page: 3542-3553
Publish at: 2025-10-01

A novel method for examining promoters using statistical analysis and artificial intelligence learning

10.11591/ijai.v14.i5.pp4006-4016
Sinan Salim Mohammed Sheet , Marwa Mawfaq Mohamedsheet Al-Hatab , Maysaloon Abed Qasim
Accurately classifying promoters has become a significant focus in bioinformatics research. Although numerous studies have attempted to address this challenge, the performance of existing methods still leaves room for improvement this study, statistical feature analysis has been applied to the features that have been developed in our previous work. This approach extracted additional informative features from basic sequence characteristics and then used them together with the original and newly engineered features. Utilizing statistical feature analysis enhanced key patterns, which lead to an improvement in the accuracy of the promoter classification. Results demonstrated that our proposed method outperforms other models that use only basic features. The value of the area under the curve (AUC) of 0.83958 achieved when using the combined feature set confirmed the effectiveness of our approach. Furthermore, the AUC value reached 1 when these optimized features were used with naive Bayes (NB) classifier, referring to the strength of incorporating statistical analysis into feature design.
Volume: 14
Issue: 5
Page: 4006-4016
Publish at: 2025-10-01

Optimizing diabetes prediction: unveiling patient subgroups through clustering

10.11591/ijai.v14.i5.pp3681-3692
Rita Ganguly , Dharmpal Singh , Rajesh Bose
Diabetes is a significant global health concern, leading to numerous deaths annually and affecting many individuals who remain undiagnosed. As its prevalence rises, the importance of early detection becomes increasingly vital. The rising diabetes epidemic demands data-driven strategies to catch health problems sooner and identify them clearly. This study utilizes the Pima Indians diabetes dataset (PIDD) to compare three powerful clustering schemes such as k-means, fuzzy C-means, and hierarchical. Uncontrolled diabetes, arising from the body's struggle to manage blood sugar due to insulin deficiency, can lead to devastating complications. Early detection and intervention are the cornerstones of effective management and improved patient outcomes. This study breaks new ground by meticulously evaluating the performance of each clustering algorithm using advanced metrics like silhouette score and adjusted Rand index. The goal is to identify the method that generates the most accurate and well-defined clusters for diabetes-related attributes. This, in turn, has the potential to revolutionize diabetes diagnosis, enabling earlier interventions and ultimately leading to better disease management and patient care. By providing a comprehensive comparison of these clustering techniques, this research offers a significant contribution to the fight against diabetes.
Volume: 14
Issue: 5
Page: 3681-3692
Publish at: 2025-10-01

Laurent series intelligent multidimensional object optimization classification for crop disease detection

10.11591/ijai.v14.i5.pp4050-4060
Anandhan Karunanithi , Ajay Shanker Singh
Rice crop disease detection and its diagnosis methods are vitally important for the agriculture field to be sustainable. Traditional methods suffer from paddy yield, complex issues, and crop diseases, leading to inefficiencies in the agriculture domain. Our research provides space for a novel approach, combining the Laurent series with an intelligent multidimensional object optimization (LIMO) classification framework based on generative adversarial networks (GANs) to recognize various types of crop diseases in agricultural fields. Through our proposed research work, IoT nodes sense the values of the field crop, and gathered information is shared with processing units through base station communication. Multi-objective and cognitive learning routing (MOCLEAR) protocol supports choosing the optimal path for data transmission improvement. Then, for image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images. After receiving segment input images, perform feature extraction and classification using significant attributes. The proposed Laurent series with IMO is newly formulated by integrating the Laurent series with Intelligent IMO algorithms. Through extensive experimentation and analysis, the proposed LIMO-based GAN network provides effective and improved performance metrics with overall accuracy, sensitivity, and specificity values at 91.5%, 92.6%, and 92.41%, respectively. 
Volume: 14
Issue: 5
Page: 4050-4060
Publish at: 2025-10-01

Artificial intelligence applications in agriculture: a systematic review of literature

10.11591/ijai.v14.i5.pp3503-3519
Michael Cabanillas-Carbonell , Joselyn Zapata-Paulini
Artificial intelligence (AI) is transforming agriculture by offering innovative solutions to persistent challenges. This systematic literature review explores the most studied AI applications in agriculture, emphasizing crop management, agronomic decision-making, early detection of diseases and pests, and climate change adaptation. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 700 publications were retrieved from databases such as Scopus, ScienceDirect, and IEEE Xplore, with 104 relevant articles selected after applying strict inclusion and exclusion criteria. The findings underscore the importance of machine learning and image processing in tailoring agronomic practices to specific plot conditions and microclimates. These tools enable early identification and control of plant diseases and pests, reducing crop losses and dependence on chemicals. Nonetheless, challenges remain, particularly regarding accessibility for smallholder farmers, high implementation costs, and limited data infrastructure. While AI offers significant potential to enhance agricultural productivity, sustainability, and resilience, addressing these limitations is crucial. A balanced, inclusive approach is essential to ensure AI’s benefits are widely distributed and contribute to long-term food security and environmental sustainability.
Volume: 14
Issue: 5
Page: 3503-3519
Publish at: 2025-10-01

Enhancing challenge-based immersion in cultural game using appreciative fuzzy logic

10.11591/ijai.v14.i5.pp3702-3714
Muljono Muljono , Hanny Haryanto , Pulung Nurtantio Andono , Raden Arief Nugroho , Fitri Yakub , Indriyo K. Sukmono
Many traditional games in Indonesia are considered cultural heritage and are in serious decline; young generations no longer know about them. Serious games have been considered a potential educational tool for cultural heritage preservation. Lack of immersive experience due to over-focus on the learning content is a common problem in those games. Very little research also discusses cultural heritage serious game design frameworks. This study uses the appreciative fuzzy logic system (AFLS) to enhance the challenge-based immersive experience (CBIE) in the Joglosemar cultural heritage game. The AFLS provides autonomous challenges, such as enemy numbers and aggressive behavior, and the frequency of item appearances in the games using fuzzy logic with respect to the appreciative serious games (ASG) concepts. The ASG is the design guide for serious games that divides the game activities into 4-D: discovery, dream, design, and destiny. We use three ASG-based serious games to evaluate the CBIE produced by AFLS. The game experience questionnaire (GEQ) is used to measure the player experience, while the cross-validation is used to measure the AFLS performance. Results show that the AFLS enhances the CBIE. The study contributes mainly to provide reliable intelligent system for automated serious game design.
Volume: 14
Issue: 5
Page: 3702-3714
Publish at: 2025-10-01

Artificial intelligence-powered smart roads: leveraging orange3 for traffic signs recognition

10.11591/ijai.v14.i5.pp3816-3826
Areen Arabiat , Muneera Altayeb , Sanaa Salama
Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.
Volume: 14
Issue: 5
Page: 3816-3826
Publish at: 2025-10-01

Accuracy of long short-term memory model in predicting YoY inflation of cities in Indonesia

10.11591/ijai.v14.i5.pp3887-3896
Harfely Leipary , Adi Setiawan
Our  research  evaluates  the  effectiveness  of  the long  short-term  memory (LSTM) model in forecasting annual year-on-year (YoY) inflation across 82 cities in Indonesia based on time series data from BPS economic reports for 2014-2024. This study tests the accuracy of the model in reconstructing past inflation patterns, then evaluates the capabilities and limitations of the model in  various  urban  area  contexts  with  the root  mean  square  error (RMSE), mean  absolute  percentage  error (MAPE),  and coefficient  of  determination(R2)  metrics.  The  findings  show  that  LSTM  performs  well  in  metropolitan areas  such  as  Jakarta,  Bandung,  and  Surabaya  with R2values  >0.8  and  the lowest  MAPE  of  10.91%  in  Jakarta.  However,  in  small  cities  with  higher economic  volatility  such  as  Tanjung  Pandan,  the  model  shows  significant prediction   errors   (R²<0.50   and   MAPE   up   to   283.11%).   Moderate performance  (0.50≤ R²≤0.80)  was  found  in  cities  such  as  Palembang, Semarang, and Makassar, reflecting the model's adaptive ability to moderate inflation  patterns.  These  results  emphasize  the  important  role  of  structured economic data in improving the reliability of predictions, so that the policy implications  of  this  study  include  the  use  of  the  LSTM  model  as  an  early warning system by fiscal and monetary authorities, as well as the need for a data-based  inflation  control  strategy  to  strengthen  regional  and  national economic    resilience    in    supporting    sustainable    development    towards Indonesia Emas 2045.
Volume: 14
Issue: 5
Page: 3887-3896
Publish at: 2025-10-01

Grid graph convolutional network-cyclical learning rate EfficientNet for liver tumor segmentation classification

10.11591/ijai.v14.i5.pp4235-4249
Sangi Narasimhulu , Ch D V Subba Rao
Liver tumors are identified in computed tomography (CT) images, which are crucial for accurate disease diagnosis and treatment planning as they enable clear delineation of tumors. Hence, it is vital in the field of medical radiology to segment and classify CT images of liver tumors effectively. However, liver tumor locations are not captured accurately at the boundaries in terms of size and depth within the liver due to downsampled images, leading to reduced segmentation and classification results. This research proposes a grid-graph convolutional network-based cyclical learning rate EfficientNet (GGCN-CLREN) to accurately segment and classify liver tumors. GGCN addresses inaccurate liver tumor segmentation due to downsampled images, which capture spatial relationships effectively and preserve tumor boundaries as well as depth information. For classification, CLREN optimizes classification by adjusting the learning rate, which enhances convergence and accuracy. Therefore, GGCN-CLREN ensures enhanced segmentation and classification by addressing size and depth inaccuracies. Golden sine gray wolf optimization (GSGWO) selects the most appropriate features effectively. The GGCN-CLREN achieves commendable accuracies of 99.80% and 99.96%, respectively, for the LiTS17 and CHAOS datasets when compared to the existing techniques: enhanced swim transformer network with adversarial propagation (APESTNet) and adding inception module-UNet (AIM-UNet).
Volume: 14
Issue: 5
Page: 4235-4249
Publish at: 2025-10-01

A hybrid model for handling the imbalanced multiclass classification problem

10.11591/ijai.v14.i5.pp3982-3993
Esra'a Alshdaifat , Fairouz Hussein , Ala'a Al-shdaifat , Malak Al-Hassan , Enshirah Altarawneh
Data in many application domains is imbalanced. In machine learning, addressing imbalanced data is crucial to prevent bias towards the dominant class label and ensure that prediction models can learn and predict the minority class proficiently. This paper proposes a hybrid imbalanced classification model (HICD) to address the multiclass imbalanced data problem. The primary idea is to combine effective methods to construct a classification model that can handle multiclass imbalanced data effectively. Four methods are employed: an oversampling method to balance the data, a decomposition method to convert the multiclass problem into a set of binary problems, ensemble classification to integrate base classifiers to improve prediction, and a boosting method to encourage the classifier to pay more attention to misclassified samples. To evaluate the proposed model, seventeen imbalanced datasets from various application domains, featuring different numbers of classes, instances, features, and imbalance ratios, are assessed. The experimental results and statistical significance tests demonstrate that the proposed hybrid model significantly outperforms the standard one-vs-one (OVO) approach and the OVO combined with oversampling technique (SMOTE), both considered state-of-the-art for addressing imbalanced multiclass datasets, in terms of F1-score.
Volume: 14
Issue: 5
Page: 3982-3993
Publish at: 2025-10-01

Anisa: artificial intelligence companion for elderly care with empathetic conversations and health management

10.11591/ijai.v14.i5.pp4260-4270
Shilpa Karegoudra , Pawan Hegde , Sinchana C. Poojary , Pranitha P. Shetty , Sahana M. Kotian , Saanvi Kallianpur , Veeresha R. Koti
This study introduces Anisa, an advanced artificial intelligence (AI) companion designed to enhance elderly care by addressing the multifaceted needs and challenges of older adults. The system integrates the Llama 3.2 model, powered by Groq, to facilitate context-aware dialogues and empathetic interactions. This capability helps alleviate loneliness and provides essential companionship. Agenda.js is used for scheduling and managing reminders, ensuring timely notifications for medications and appointments. Additionally, Twilio enables emergency alerts when distress signals are detected. Anisa promotes physical activity, tracks daily routines, and generates activity reports shared with caregivers and healthcare providers. Expo CLI implements step-tracking and document-sharing features. By integrating these functionalities, Anisa improves the quality of life for seniors, eases caregiver responsibilities, and fosters a safer, more supportive environment.
Volume: 14
Issue: 5
Page: 4260-4270
Publish at: 2025-10-01

Transformation of Islamic values in the era of artificial intelligence

10.11591/ijai.v14.i5.pp4353-4362
Nur Faizin , Abul Ma`ali , Muhammad Fahmi Hidayatullah , Ahmad Munjin Nasih , Rohmatul Faizah , Moh. Fauzan
The emergence of artificial intelligence (AI) such as ChatGPT has brought significant changes in the way humans’ access and understand information, including in the religious field. This research aims to examine how the transformation of Islamic values occurs through ChatGPT responses in the aspects of educational ethics, Islamic law, da'wah, and Qur'anic interpretation. This study applied a qualitative case study method and data was collected from indexed scientific articles from academic databases, ChatGPT responses, and online news articles. The study findings show that the use of ChatGPT in the context of Islam requires caution. While technology can answer a variety of questions, there are fundamental flaws related to the accuracy of citations, unverified sources of information, and a lack of understanding of the sharia context. In fact, there are errors in the mention of Qur'anic verses that have the potential to cause confusion. This emphasizes the importance of the sanad principle in Islamic scholarship as a valid reference. The paper proposes the need to develop more ethical and contextual AI systems in understanding religious questions, as well as the involvement of scholars and academics in training machines to conform to Islamic values.
Volume: 14
Issue: 5
Page: 4353-4362
Publish at: 2025-10-01

Data-driven clustering and prediction of high school graduation rates in Indonesia (2015-2023) using machine learning

10.11591/ijai.v14.i5.pp3771-3780
Muhammad Salman Arrosyid , Marzuki Marzuki , Widihastuti Widihastuti , Haryanto Haryanto , Maria Angelina Fransiska Mbari
This study aims to analyze the graduation rate of senior high school education in 34 Indonesian provinces during the period 2015-2023 and identify patterns of educational disparities between regions. To achieve the objectives, this study applies a neural network to predict education completion patterns based on historical data, then the prediction results are analyzed using K-means clustering technique utilizing the elbow method to select the ideal number of clusters. The clustering results show three categories of provinces based on education completion rates: high, medium, and low. The provinces with high completion rates, generally, supported with good education infrastructure and effective policies, while the medium category faces challenges in resource distribution, but still potentially improve. In contrast, the low category suffers from limited access, geographical constraints, and socio-economic disparities. This research contributes to education policy-making by offering a machine learning-based approach to understanding education disparities between regions. The new insight offered by this study lies in the integration of neural network and K-means clustering in mapping education completion rates to support strategies for improving access and quality of education in Indonesia.
Volume: 14
Issue: 5
Page: 3771-3780
Publish at: 2025-10-01

FaceSynth: text-to-face generation using CLIP and its variants with generative adversarial networks

10.11591/ijai.v14.i5.pp3588-3598
Priyadharsini Ravisankar , Shruthi Dhanvanth , Vaishnave Jenane Padmanabhan
In recent years, there have been massive developments in the field of generative AI, especially in generative adversarial networks (GANs). GANs generate original images that haven't been seen during training and have had several advancements like StyleGAN, StyleGAN2, and StyleGAN2-adaptive discriminator augmentation (ADA). Contrastive language-image pre-training (CLIP), by OpenAI, is a visual linguistic model that has been trained to associate texts with images. Recently, new CLIP variants were developed, such as metadata-curated language-image pre-training (MetaCLIP), released by Facebook and trained on a larger dataset, and Multilinigual-CLIP, which adapts CLIP to multiple languages. We compare CLIP and its variants in text-to-face synthesis with a custom StyleGAN2-ADA model and a pre-trained StyleGAN2 model. Our training-free algorithm starts with an initial image latent code that is iteratively manipulated to match a given text description. It achieves this by minimizing the distance between the text and image embedding in the multi-modal embedding space of the CLIP models. An examination of CLIP and its variants showed that MetaCLIP outperformed its competitors in LPIPS similarity and closeness of the synthesized image to the actual prompt. CLIP produced the most realistic images with the best FID score and multilingual-CLIP presented a choice of input text language and generated decent images.
Volume: 14
Issue: 5
Page: 3588-3598
Publish at: 2025-10-01
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