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31,377 Article Results

RGB-D salient object detection with local feature and semantic segmentation

10.11591/ijai.v15.i3.pp2774-2785
Zhang Wang , Kim On Chin , Rayner Alfred , Junyi Chai , Rundong Zhang , Soo See Chai
Red, green, blue–depth (RGB-D) salient object detection (SOD) focuses on identifying visually prominent objects by simulating human visual perception. While existing RGB-D SOD methods have demonstrated results, there remain challenges in effectively leveraging extrinsic cues and enhancing feature representation. To address these limitations, novel RGB-D SOD model with local feature extraction and semantic segmentation (LFSS) is introduced, which is built on an encoder-decoder architecture. The encoder preprocesses the input images by merging RGB and depth data through a channel and spatial attention (CSA) module. A local feature extraction module further refines this fusion. The decoder consists of three key modules: i) the multi-feature extraction (MFE) module enhances base features through diverse convolutional operations; ii) the semantic segmentation enhancement (SSE) module optimizes features via spatial pyramid pooling and atrous convolution; and iii) the local/global agreement and edge detection (LGE) module that enables multi-level feature interaction and edge detection. These modules work sequentially to enhance and extract salient objects. LFSS is evaluated on six standard RGB-D SOD datasets (NJU2K, NLPR, STERE, LFSD, SSD, SIP) by four metrics, outperforming the comparison models with up to 1.2% F-measure improvement. LFSS is found to be a versatile model, offering valuable applications in engineering.
Volume: 15
Issue: 3
Page: 2774-2785
Publish at: 2026-06-01

Computed tomography imaging radiomics: a novel approach to early-stage non-small cell lung cancer prediction

10.11591/ijai.v15.i3.pp2471-2483
Raviteja Balekai , Mallikarjun S. Holi
Radiomics shows promise as non-invasive method for enhancing clinical staging of non-small cell lung cancer (NSCLC) by using quantitative information from computed tomography (CT) scans. This study presents radiomics-based machine learning (ML) approach for staging NSCLC patients into clinical stages I, II, and III based on shape, intensity, and texture features. CT images of 369 NSCLC patients are collected from the cancer imaging archive (TCIA), and extracted 107 radiomic features following image biomarker standardization initiative (IBSI) protocol. The analysis of the sources of variability due to different imaging protocols, using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), showed that these effects were resolved through ComBat harmonization. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) are used for feature selection. Five ML algorithms: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) were used, with an 80:20 train-test split and 10-fold cross-validation. The classifier is assessed using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic (AUROC) curve. The RFE and RF classifier combination performed the best with AUROC of 0.9307 and accuracy of 0.8114. This study illustrates the use of radiomics models in non-invasive classification of NSCLC stages and it is role in clinical decision making.
Volume: 15
Issue: 3
Page: 2471-2483
Publish at: 2026-06-01

Engineering intelligence for a sustainable and resilient future: from foundations to real-world impact toward the SDGs

10.11591/ijece.v16i3.pp1075-1084
Tole Sutikno
The June 2026 issue of this journal presents a comprehensive body of research advancing efficient engineering intelligence from foundational theory to real-world deployment, with strong alignment to the Sustainable Development Goals (SDGs). A significant cluster addresses SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) through innovations in microbial fuel cells, high-voltage insulation reliability, artificial intelligence (AI) based battery management systems, and energy-efficient LoRa/LoRaWAN frameworks. These works emphasize energy sustainability, system resilience, and infrastructure optimization. A second cluster focuses on advanced electronics, control, and communication systems, including memcapacitor design, hybrid model predictive control, reflectarray antennas, and embedded intelligence for autonomous systems, demonstrating efficiency-driven engineering across hardware and system levels. A dominant cluster highlights SDG 3 (Good Health and Well-being), with applications in medical imaging, sepsis detection, breast cancer classification, and mental health analysis, leveraging deep learning, transformers, and hybrid AI models. Finally, contributions aligned with SDG 4 (Quality Education) explore gamified learning systems, virtual reality adoption, and SDG-integrated educational information systems, while complementary studies in agriculture, finance, and Internet of Things (IoT) further demonstrate the societal impact of intelligent systems. Collectively, these works reinforce the role of efficient, scalable, and data-driven engineering in addressing global challenges.
Volume: 16
Issue: 3
Page: 1075-1084
Publish at: 2026-06-01

A survey of retrieval algorithms in ad and content recommendation systems

10.11591/ijece.v16i3.pp1518-1530
Yu Zhao , Fang Liu , Yuan Yuan , Yifan Dang
This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
Volume: 16
Issue: 3
Page: 1518-1530
Publish at: 2026-06-01

Effectiveness of hypnotherapy as complementary intervention in reducing post-cesarean pain: A PRISMA-based systematic review of literature released during the 2015-2024 period

10.11591/ijphs.v15i2.26893
Annes Rindy Permana , Widigdo Rekso Negoro , Sindu Sintara , Muhammad Rodli , Suryanto Suryanto , Reko Priyonggo
Cesarean section is a common surgical procedure often associated with moderate to severe postoperative pain. Hypnotherapy has been proposed as a complementary, non-pharmacological intervention; however, evidence specific to post-cesarean pain management remains limited. The purpose of this PRISMA-based systematic review was to evaluate hypnotherapy as an intervention for reducing postoperative pain associated with cesarean section. Literature searches were conducted in Google Scholar and PubMed for studies published between 2015 and 2024 using predefined inclusion and exclusion criteria. From 326 identified records, four studies met all eligibility criteria and were included in the qualitative synthesis. Despite heterogeneity in study design and intervention protocols, all included studies consistently reported reductions in postoperative pain intensity among participants receiving hypnotherapy compared with standard postoperative care, with pain outcomes primarily measured using validated scales such as the Numerical Rating Scale. These findings suggest that hypnotherapy may support conventional analgesic strategies as an adjunctive intervention during early postoperative recovery. Nevertheless, the scarcity of existing research and the presence of methodological limitations suggest the need for rigorously designed randomized controlled trials to provide more robust evidence.
Volume: 15
Issue: 2
Page: 474-483
Publish at: 2026-06-01

Intestinal disorders categorization in endoscopic images using deep learning architectures

10.11591/ijai.v15.i3.pp2347-2356
Esha Saxena , Suraiya Parveen , Mohd. Abdul Ahad , Meenakshi Yadav , Mohammad Anas
Gastroenterology is revolutionized by advancements in artificial intelligence (AI). As the gastrointestinal (GI) tract is consulted, globally 40% of the world's and 18% of the Indian population are affected. AI is a reliable sword for diagnosing issues related to the GI tract. The learning capabilities of deep learning (DL) techniques make it widely helpful in medical investigations. The variety of data available in the medical sector generates the need for an appropriate model for every problem domain. The purpose of this research is to explore the significance of medical image pre-processing and the implementation of pre-trained DL models on endoscopic images for the diagnosis of disease. Convolutional neural network (CNN)-based architectures have robust diagnostic potential for medical images. It can assist physicians as a tool for disease analysis, screening and help in investigating further needs. The paper also provides a comparative performance framework showing CNN architectures and preprocessing techniques for endoscopic images to highlight the key points important for investigating GI tract related diseases. The endoscopic images were trained over VGG-16, ResNet-50 and DenseNet-121, DL models. The result suggests that VGG-16 and ResNet-50 gave promising results with an accuracy maximum of 87.50%.
Volume: 15
Issue: 3
Page: 2347-2356
Publish at: 2026-06-01

Hybrid LUT–CORDIC architecture on FPGA for efficient and accurate trigonometric computation in robot manipulators

10.11591/ijra.v15i2.pp377-387
Nia Gella Augoestien , Jazi Eko Istiyanto , Ahmad Ashari , Andi Dharmawan
Although computational resources on robots are often limited, real-time, accurate computation of trigonometric functions is essential in robot manipulators, particularly for forward and inverse kinematics, dynamic analysis, trajectory planning, and motion control. The LUT method requires a large number of LUTs to improve accuracy. The accuracy of the CORDIC method is highly dependent on the number of computational latencies, which affects the computation speed. This paper combines two general approaches for computing trigonometric functions on robot manipulators that improve accuracy without increasing resource utilization and computational latencies. The design uses a 10-bit format (0.125° input resolution and 2-10 output precision) and is implemented in VHDL on a Xilinx Artix-7 XC7A100T-CSG324 FPGA. Compared with a CORDIC-only baseline, the maximum absolute error is reduced from 0.083007812 to 0.009801151 for sine and from 0.079101563 to 0.008901377 for cosine, while MSE drops from 2.4031×10-4 and 2.32974×10-4 to 5.87754×10-6 and 5.87862×10-6, respectively. The hybrid core also reduces slice usage from 81 to 69 and shortens computation time from 35.271 ns to 30.627 ns, making it suitable for resource-constrained real-time robotic control.
Volume: 15
Issue: 2
Page: 377-387
Publish at: 2026-06-01

Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey

10.11591/ijece.v16i3.pp1236-1248
Pang Wai Leong , Raymond Chia , Phang Swee King , Goh Hui Hwang , Chan Kah Yoong , Chung Gwo Chin
A key foundation of 5G heterogeneous networks (HetNets) is the use of network slicing, which divides bandwidth into multiple logical networks and accounts for each function’s requirements. Currently, various machine learning (ML) models are being implemented into the network slicing algorithm to allocate bandwidth dynamically. The network slicing algorithm analyzes the traffic and allocates bandwidth based on the current services using a network-centric approach. However, limited work is found on further studying the impact of user-centric algorithms in bandwidth allocation. This paper presents the network slicing used in 5G and the limitations of these algorithms. A detailed review of user-centric bandwidth allocation algorithms is presented, along with a critical review of ML algorithms for traffic prediction and resource allocation decisions. Finally, the technology gaps and opportunities of the existing works are reported, and the direction for further research of ML in user-centric bandwidth allocation algorithms is tabulated.
Volume: 16
Issue: 3
Page: 1236-1248
Publish at: 2026-06-01

Smart water distribution for smart cities based on Internet of Things

10.11591/ijece.v16i3.pp1655-1668
Amal Douli , Khelifa Benahmed , Belkacem Draoui
Against an unprecedented water crisis in our country, balancing water supply and demand is necessary for a secure and sustainable water supply. This challenge requires systems capable of delivering the necessary quantities while conserving resources. Numerous research initiatives focus on addressing water distribution challenges with the help of smart water systems to optimize network operations and minimize water demand. Based on these advancements, this paper proposes a new smart water distribution system for southwest of Algeria. The system integrates the Internet of Things (IoT), information and communication technologies, and smart technologies to address critical attributes for enhancing efficiency. To achieve the efficient management of two-way flows (both water and data) based on water demand and its availability, two innovative architectures have been proposed, using various measurements of water quantity and quality parameters. Algorithms to automate and optimize water distribution are also proposed. According to obtained results, performance has improved, with an accuracy rate of over 98%. These results establish the suggested system as a strong option for intelligent and sustainable water resource management by demonstrating its efficacy and durability.
Volume: 16
Issue: 3
Page: 1655-1668
Publish at: 2026-06-01

Enhancing fake news detection: a hybrid BERT-XGBoost model for improved performance and interpretability

10.11591/ijai.v15.i3.pp2385-2397
Nishant Vasantkumar Hegde , Suneesh Bare , Namruth Reddy , Rajat Gondkar Aravinda , Minal Moharir , Aamir Ibrahim
The widespread spread of fake news poses a serious threat to the integrity of information. The dominant approach to detection involves end-to-end fine-tuning of large transformer models like bidirectional encoder representations from transformers (BERT), which, despite achieving high accuracy, often function as opaque “black boxes” with limited interpretability. This paper proposes and validates a hybrid, decoupled architecture that proves to be a more practical and powerful alternative. We first fine-tune a DistilBERT model on the full WELFake dataset of 71,537 articles after cleaning to create domain-specific embeddings. These high-dimensional vectors are then used as input features to train a robust extreme gradient boosting (XGBoost) classifier. The results demonstrate that the hybrid model achieves a state-of-the-art accuracy of 99.76%, slightly surpassing the already high performance of a standard end-to-end fine-tuned model. Crucially, this approach provides this top-tier performance while offering significant advantages in model interpretability through feature importance analysis. This work establishes that a decoupled architecture is not just a viable alternative but a superior practical strategy for combating misinformation, successfully balancing state-of-the-art accuracy with essential model transparency.
Volume: 15
Issue: 3
Page: 2385-2397
Publish at: 2026-06-01

Optimization of transfer learning for facial emotion classification on the FER-2013 dataset

10.11591/ijece.v16i3.pp1213-1226
Nida Muhliya Barkah , Shofwatul ‘Uyun
Facial expressions play a key role in non-verbal communication by naturally reflecting human emotions. Facial emotion recognition (FER) using computer vision has gained attention with advances in deep learning. However, deep learning models require large datasets to perform well, posing a challenge for FER tasks with limited data. Transfer learning is a promising approach to address this issue, but a standardized method for FER is yet to be established. This study optimizes three transfer learning models ResNet-50, Inception V3, and Xception on the FER-2013 dataset. Experiments include testing input image sizes, hyperparameter tuning, data augmentation, layer addition, and training methods. Results show each model requires different input sizes for best accuracy. Hyperparameter tuning improves accuracy by 6.35%, 4.69%, and 1.04% for ResNet-50, Inception V3, and Xception, respectively. Augmenting only the disgust class yields better accuracy than augmenting all classes. The freeze fine-tuning method is less effective than fine-tuning alone on datasets with thousands of samples but outperforms the freeze layer method. The best accuracies achieved are 64.89% (ResNet-50), 65.83% (Xception), and 66.40% (Inception V3). These findings provide insights into freeze fine-tuning limitations and guidance for optimizing transfer learning in FER with limited data.
Volume: 16
Issue: 3
Page: 1213-1226
Publish at: 2026-06-01

Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS

10.11591/ijece.v16i3.pp1319-1328
Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Rujianto Eko Saputro
The introduction of gamified massive open online courses (G-MOOCs) represents a novel advancement in technical and vocational education and training (TVET). The use of gamification in education has been shown to increase engagement and motivation, which are crucial for effective learning. However, there is limited research on the specific impacts of G-MOOCs on learner outcomes in TVET. A key feature of G-MOOCs is the integration of gamification elements to enhance learner engagement and interest. This research employs structural equation modelling with partial least squares (SEM-PLS) to examine learners' perceptions of their participation and learning experiences in G-MOOCs for TVET. Specifically, the study aims to identify how gamification approaches such as fun, engagement, and learner interaction influence knowledge acquisition, skills development, satisfaction, and overall learning outcomes. The analysis reveals that G-MOOCs have a strong positive correlation (0.505) with learning engagement. Additionally, learning engagement significantly moderates learning outcomes (p=0.002). Interaction also has a significant impact (p=0.381) on learning outcomes. Overall, the findings indicate a significant positive relationship between learners' activities and their performance in G-MOOCs.
Volume: 16
Issue: 3
Page: 1319-1328
Publish at: 2026-06-01

Performance comparison of deep learning models for concrete crack detection on mobile devices

10.11591/ijai.v15.i3.pp2811-2825
Sarapee Chunkaew , Somporn Ruang-On , Prawit Nuengmatcha , Kritaphat Songsri-in
Concrete crack detection is essential for structural maintenance, yet traditional manual inspection methods are time-consuming and require specialized expertise. While deep learning offers promising solutions, existing models often demand high computational resources unsuitable for mobile deployment. This research evaluates three convolutional neural network (CNN) architectures, namely mobile network (MobileNet), visual geometry group-16 (VGG-16), and residual network-50 (ResNet-50), to identify an optimal model for practical mobile-based crack detection. A dataset of 1,634 images was collected from online databases and field documentation, categorized into 10 classes across three severity levels: i) severe cracks requiring urgent repair (30%); ii) cracks requiring monitoring (40%); and iii) minor cracks (30%). The models were trained using standardized parameters with 224×224-pixel RGB input, rectified linear unit (ReLU) activation, and softmax classification. Systematic parameter optimization was conducted across epochs, learning rate, dropout rate, and optimizer selection, with stochastic gradient descent (SGD) identified as the optimal optimizer. Experimental results demonstrate that MobileNet achieves the best performance with 80% accuracy and a compact model size of 13.1 megabytes. This study concludes that MobileNet provides an optimal balance between detection accuracy and computational efficiency, enabling practical field deployment for automated concrete crack detection, with expert verification recommended for critical structural assessments.
Volume: 15
Issue: 3
Page: 2811-2825
Publish at: 2026-06-01

Designing and evaluating a community-based digital dictionary system for the Balinese language: An IT innovation adoption study

10.11591/ijece.v16i3.pp1369-1381
Cokorda Pramartha , Madek Jeani Purnama , Ida Bagus Gede Sarasvananda , I Wayan Arka , Ni Luh Watiniasih
Regional and vulnerable languages increasingly depend on digital tools to remain visible and usable in everyday life, yet many dictionary initiatives are described mainly in terms of content or interface features rather than evaluated as information-system innovations. This paper presents an exploratory design science study of a community-based Balinese digital dictionary that supports bidirectional Balinese-Indonesian lookup, Latin and Balinese Unicode script, speech-level information, part-of-speech tagging, related-word search, and role-based contribution workflows. The platform is implemented as a web-based system with a three-tier architecture and relational database. To evaluate adoption readiness, 40 users completed representative tasks and then responded to an adapted Moore and Benbasat IT innovation adoption instrument covering seven constructs. The results show high ease of use, relative advantage, and compatibility, indicating strong functional value and fit with user routines. Image and visibility are moderate, while result demonstrability and visibility show lower reliability and are therefore interpreted as exploratory indicators. The study contributes both a documented digital-dictionary artefact for Balinese language support and a reusable evaluation approach for other early-stage community-facing information and communication technology (ICT) systems. The findings suggest that wider uptake depends not only on technical quality, but also on institutional visibility, outreach, and continued content enrichment.
Volume: 16
Issue: 3
Page: 1369-1381
Publish at: 2026-06-01

AI-enabled energy-aware routing approach for future-wireless sensor networks

10.11591/ijece.v16i3.pp1543-1561
Shamsher Singh , Mandeep Kumar
Next-generation wireless sensor networks (WSNs) demand intelligent, energy-aware communication mechanisms capable of sustaining long-term operation in environments with varying conditions and strict resource limitations. Traditional routing protocols often fail to optimize energy consumption under varying network densities, heterogeneous traffic patterns, and environmental uncertainties. This research proposes an AI-enabled energy-efficient routing protocol (AI-EERP) designed to enhance network lifetime, stability, and data delivery performance in next-generation WSNs. The protocol integrates machine learning–based node selection, adaptive clustering, and predictive residual-energy estimation to make optimized routing decisions in real time. Using AI-driven models, AI-EERP dynamically adjusts routing paths based on energy patterns, link quality, and network topology changes. The simulation outcomes clearly indicate that the proposed approach achieves notable gains in energy efficiency, packet delivery reliability, and network lifetime when compared with traditional routing protocols, including LEACH, PEGASIS, and HEED. The proposed approach establishes a robust and scalable framework for future intelligent WSN deployments across applications including smart cities, precision agriculture, environment-focused applications and automated industrial operations.
Volume: 16
Issue: 3
Page: 1543-1561
Publish at: 2026-06-01
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