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Video summarization using deep image captioning models

10.11591/ijeecs.v43.i2.pp547-554
Qudes M. B. Aljelawy , Sarah S. Mohammed , Entessar K. Hanoun
This research presents a novel approach for video summarization by leveraging deep image captioning models. A pretrained image captioning model, namely Salesforce's bootstrapped language image pretraining (BLIP), is used to extract keyframes from a video at regular intervals and produce natural language descriptions. These textual descriptions are then filtered for non-repetition and concatenated into a coherent summary, allowing users to understand the video’s content without viewing it in full. The proposed framework aims to improve video browsing, indexing, and retrieval efficiency, particularly for big datasets. The proposed method achieves a significant reduction in redundancy by 40% compared to raw captioning sequences. Evaluation using semantic consistency checks demonstrates that the BLIP-based framework maintains high descriptive accuracy even in complex scenes, providing a scalable solution for large-scale video indexing.
Volume: 43
Issue: 2
Page: 547-554
Publish at: 2026-08-01

An overview of SSP structures in dragon graphs and domination and independent domination fuzzy SSP graphs

10.11591/ijeecs.v43.i2.pp485-494
R. Mary Jeya Jothi
Social network modeling is one of the many applications of dominant sets in graph theory. Social networks are made up of personalities or sets of individuals linked by relationships; graph theory provides a useful framework for examining and simulating these structures. We have already covered the idea of domination and how to calculate the domination numeral for various fuzzy graphs. Unlike anything found in the literature to date, we presented the idea of domination of fuzzy SSP graphs in this study. It is also spoken about the several super strongly perfect (SSP) structural parameters in fuzzy SSP graphs. The study appears to contribute to the ground of theory of graphs by extending the notion of domination to fuzzy SSP graphs. The paper also covers how to acquire specific dominant sets, coloring numbers, and cliques (maximal) in the context of fuzzy SSP graphs. In these kinds of scenarios, researchers can employ the following broad techniques to identify dominating sets.
Volume: 43
Issue: 2
Page: 485-494
Publish at: 2026-08-01

Low-cost real-time campus assistive navigation device for the visually impaired: The University of Ilorin case study

10.11591/ijeecs.v43.i2.pp555-567
Mahmud Hafeez Owolabi , Idajili John Ojochegbe , Ayinla Shehu Lukman , Jimoh-Mahmud Aishat Oladayo , Yusuf Abdulrahman Olalekan , Olaogun Jerry Oluwajomiloju
Navigating dynamic environments remains a significant challenge for visually impaired individuals due to limited spatial awareness, which restricts their mobility, independence, and safety. Traditional aids such as white canes and guide dogs provide physical support but lack contextual feedback. While recent advances in artificial intelligence (AI) and computer vision (CV) have enabled real-time sensing, many assistive systems remain costly, complex, and dependent on continuous network connectivity. This study introduces a low‑cost, portable campus assistive navigation (CAN) device that delivers real‑time perception and offline guidance. The system employs a custom dataset, an optimized YOLOv11 model, and a Pi Camera for continuous object detection, supported by an HC‑SR04 ultrasonic sensor for obstacle avoidance and auditory alerts. Training and validation confirmed robust convergence, with precision ~0.95, recall near 1.0, and mAP@0.5 exceeding 0.9, while mAP@0.5:0.95 remained above 0.8, demonstrating reliable detection and generalization under strict thresholds. Field tests further reported confidence scores of 0.74–0.84, 98% accuracy in distance measurement, and GPS localization within ±1.5 m. Real‑time auditory and haptic feedback via Bluetooth headphones enhanced mobility and safety. The CAN device offers a scalable, affordable solution for autonomous navigation in campus environments.
Volume: 43
Issue: 2
Page: 555-567
Publish at: 2026-08-01

Forecasting income inequality in Vietnam’s regions using machine learning

10.11591/ijai.v15.i4.pp3464-3475
T. Thai-Phuong , L. Nguyen-Son
This study applies artificial neural networks (ANN) and support vector regression (SVR) to forecast the Gini coefficient across Vietnam’s seven socio-economic regions using limited data from 2014–2022 (N = 63). Despite the small dataset, robust techniques including repeated cross-validation, temporal backtesting, regularization, and bootstrapping were employed to mitigate overfitting. The results show a modest continued decline in income inequality through 2025, with faster improvements in urban regions (Red River Delta and Southeast) than in rural and highland areas. ANN outperformed SVR with an average cross-validation coefficient of determination (R²) of 0.93. The novelty lies in its granular regional forecasting and region-specific policy recommendations beyond national-level analyses. Key drivers—per capita income and simple housing—support targeted interventions such as housing credits in the Mekong River Delta and agricultural investments in the Central Highlands. The findings provide an evidence-based foundation for regionally tailored strategies to reduce income disparities in Vietnam.
Volume: 15
Issue: 4
Page: 3464-3475
Publish at: 2026-08-01

Hybrid deep learning model for the task scheduling in cloud computing

10.11591/ijai.v15.i4.pp3683-3691
Kavita Rani , Om Prakash Sangwan , Ritu Garg
Task scheduling plays a crucial role in optimizing performance, reducing costs, and enhancing system reliability by efficiently allocating resources to workloads. Traditional task scheduling methods lack the ability to efficiently manage workloads and resource distribution, leading to potential inefficiencies in performance and energy consumption. To address these limitations, advanced techniques leveraging deep learning and reinforcement learning are explored. This study proposes a deep learning-based model for task scheduling in cloud computing. The model employs a convolutional neural network (CNN) to predict the optimal machines for task allocation. Additionally, Q-learning is integrated with CNN to facilitate load shifting between machines, ensuring efficient utilization of resources. The dataset used in this work consists of task attributes, such as execution time, resource requirements, which were loaded from a CSV file. Comparative analysis with existing models shows that the proposed approach achieves approximately 94% accuracy and consumes less energy than other models, demonstrating its effectiveness in cloud task scheduling.
Volume: 15
Issue: 4
Page: 3683-3691
Publish at: 2026-08-01

Immuno-bioinformatics analysis of progressive alignment and logic learning machine-derived viral conserved sequences

10.11591/ijai.v15.i4.pp3189-3201
Felza Ridho , Mohammad Isa Irawan , Nurul Hidayat , Awik Puji Dyah Nurhayati
This study proposes a method to identify conserved sequence positions in viral data, with the primary goal of facilitating their translation into proteins. The approach aims to support the early detection of sub-sequences that hold promise as vaccine candidates. The method involves five key steps. First, mutation analysis using the Kimura model: analyzed mutations in the viral data using the Kimura model to provide insights into sequence variations. Second, alignment method selection to align sequences effectively, the progressive alignment approach with the neighbor-joining algorithm was chosen. Third, hybrid algorithm for identifying unchanged sequences: a hybrid algorithm that combines progressive alignment and a logic learning machine (LLM) was employed to identify unchanged sequences. Fourth, determining conserved sequences: based on the longest unchanged sequence, conserved regions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data were identified. Fifth, immuno-bioinformatics analysis for vaccine candidate peptides: using immuno-bioinformatics, protein sequences were analyzed to identify potential vaccine candidates based on B cell epitopes. Experimental validation confirmed the algorithm’s ability to identify candidate proteins and generate vaccine-worthy peptides from conserved protein sequences. The streamlined approach focuses on conserved protein sequences, ensuring efficient and targeted identification of vaccine candidates. By leveraging these conserved regions, contributions can be made to the expedited development of vaccines.
Volume: 15
Issue: 4
Page: 3189-3201
Publish at: 2026-08-01

Analysis of normalization technique on multi objective preference analysis method

10.11591/ijai.v15.i4.pp3827-3835
Fristi Riandari , Gabriel Ardi Hutagalung , Ferry Fachrizal
Normalization is a critical step in multi-criteria decision analysis (MCDA) because it influences ranking consistency and decision reliability. This study evaluates the effects of four normalization techniques linear max, linear max-min, linear sum, and semi-linear vector, within the multi objective preference analysis (MOPA) framework using a tourism development case involving 18 alternatives and 8 decision criteria with both cost and benefit attributes. The techniques were compared based on ranking behavior, discriminative capability, and robustness using statistical and non-parametric validation. The results show that linear max-min normalization provides the strongest discriminative performance and the most significant statistical results, while semi-linear vector demonstrates high ranking stability and balanced sensitivity. In contrast, linear sum and linear max exhibit lower discriminative capability under the evaluated conditions. Kendall's tau and robustness analyses further confirm that normalization choice significantly affects ranking consistency and decision reliability. These findings provide practical guidance for selecting appropriate normalization techniques and support the development of more reliable MCDA-based decision-making models for complex applications, including sustainable tourism planning.
Volume: 15
Issue: 4
Page: 3827-3835
Publish at: 2026-08-01

Intravenous immunoglobulin resistance prediction in Kawasaki disease using oversampled transformer embeddings

10.11591/ijai.v15.i4.pp3944-3954
Namitha Thattarassery Nanappan , Raghavendra Srinivasaiah , Vinith Rejathalal
Kawasaki disease (KD) is a leading cause of acquired heart disease in children under five. Although intravenous immunoglobulin (IVIG) treatment is usually effective, 10–20% of cases are resistant and at higher risk for coronary complications. Early prediction of IVIG resistance is critical but difficult due to the rarity of KD and imbalanced clinical data. To address this, we propose a novel technique called sentence transformer embeddings with synthetic minority over-sampling technique (SMOTE) oversampling (STESO), which leverages the complementary strengths of transformer-based representation learning and synthetic oversampling. Pretrained models such as paraphrase-MiniLM-L3-v2 are used to convert tabular clinical data into dense text-based embeddings, capturing deeper semantic relationships across features. By coupling these rich embeddings with SMOTE, we balance class distributions directly in the semantic space, enabling traditional machine learning (ML) models to more effectively detect minority (resistant) cases. This synergy yielded substantial improvements in sensitivity and F1-score, with random forest (RF) combined with STESO (RF-STESO) achieving the highest overall performance. Among the models evaluated, our proposed model attained best result as accuracy of 0.85, a sensitivity of 0.81, a specificity of 0.89, and an F1-score of 0.85. Our results underscore that the joint use of transformer embeddings and oversampling is more effective than either approach in isolation, offering a promising pathway for rare disease prediction tasks such as IVIG resistance prediction in KD.
Volume: 15
Issue: 4
Page: 3944-3954
Publish at: 2026-08-01

A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/44980
Raden Sutiadi , Sparisoma Viridi , Giyanto Giyanto , Andarta F. Khoir , Rizkie S. Utama , Elsa D. Aulia , Tri A. Hadi , Ludi Parwadani Aji
Manual coral point count with excel extension (CPCe) analysis requires approximately 4–6 hours to process one 50-image station, limiting the scale of coral reef monitoring. This study presents an artificial intelligence-based workflow using YOLOv8 with an EfficientNet-B0 backbone to automate benthic cover estimation. A total of 8,147 underwater images from 151 transects across 39 Indonesian coral reef stations were annotated into eleven benthic categories for model training, while evaluation was conducted using the 2021 Derawan Islands dataset. During validation, YOLOv8 achieved an average mAP@0.5 of 0.58, recall of 0.79, and an average absolute percentage cover error of 9.8% compared with CPCe. The model processed each image in 3.13 seconds, equivalent to 156.47 seconds per 50-image station, representing a 92–138× speedup over manual CPCe analysis. These results show that the proposed workflow can support scalable and near-real-time coral reef monitoring across Indonesia.
Volume: 43
Issue: 2
Page: 628-639
Publish at: 2026-08-01

Transformer-based sentiment modeling for identifying cross country fintech perception gaps

10.11591/ijeecs.v43.i2.pp522-533
Kayla Zhafira Ardinov , Muhardi Saputra , Riska Yanu Fa’rifah
Conventional sentiment analysis lacks granularity for capturing detailed user experiences and cross-country comparative insights in digital finance. This study identifies and maps perception gaps among ShopeePay users in Indonesia and Thailand using a topic-informed sentiment analysis pipeline inspired by aspect-based sentiment analysis (ABSA) principles. Adopting the knowledge discovery in databases (KDD) framework, over 170,000 web scraped reviews were preprocessed, automatically labeled through pseudo labeling, and balanced using random oversampling. To mitigate pseudo-label reinforcement, manual validation on 500 reviews per country achieved agreement rates of 98.80% (Indonesia) and 99.20% (Thailand) with Cohen’s Kappa above 0.97. The fine-tuned DistilBERT model achieved accuracies of 97.65% (Indonesia) and 98.38% (Thailand), though these figures should be interpreted within the pseudo-labeled evaluation context. Significant perception gaps were revealed: Thai users showed lower satisfaction with transactions (67.6% negative) while Indonesian users were more positive (93.0% positive). Process time received negative dominance in both countries, with Indonesia at 78.2% and Thailand at 50.8% negative. These findings demonstrate that user satisfaction is shaped by local infrastructure and cultural contexts, providing strategic insights for regional fintech development.
Volume: 43
Issue: 2
Page: 522-533
Publish at: 2026-08-01

Adaptive feature selection for credit scoring models

10.11591/ijeecs.v43.i2.pp507-521
Mostafa Mohamed SeifElnasr , Yasser Omar , Saleh Mesbah
Accurate credit scoring and delinquency prediction are critical for financial institutions, particularly when evaluating both banked and unbanked clients. Traditional feature selection approaches such as wrapper, filter, and embedded methods often require repeated retraining and may not efficiently explore optimal feature subsets in multi-class credit risk settings. This study proposes a reinforcement learning (RL)–based adaptive feature selection framework using tabular Q-learning to dynamically identify informative feature subsets for credit bucket classification and delinquency prediction. The framework was evaluated on two datasets, including proprietary credit datasets (24,533 records with 18–19 features). Using stratified 10-fold cross-validation, the proposed approach achieved 62.84% accuracy (F1=0.63) for 8-class credit bucket prediction, outperforming traditional wrapper-based methods by up to 4.2% while reducing computational cost compared to exhaustive subset evaluation. For delinquency prediction, the model achieved approximately 70% F1-score, demonstrating improved minority-class sensitivity. Compared to filter and embedded methods, the RL-based framework produced more compact feature subsets while maintaining competitive computational efficiency. These findings demonstrate that adaptive RL driven feature selection provides a practical and scalable mechanism for enhancing predictive performance in credit risk modelling, supporting automated and consistent decision making in financial institutions.
Volume: 43
Issue: 2
Page: 507-521
Publish at: 2026-08-01

PID-based performance optimization of ultra-wideband microstrip patch antennas for indoor positioning systems

10.11591/ijeecs.v43.i2.pp450-459
Fredelino A. Galleto Jr. , Aaron Don M. Africa , Bettina Gaille H. Dayrit , Gia Kyla S. Guevarra , Chrismon Elijah Q. Mansilungan , Michael Angelo M. Obciana , Mariah Venice A. Rodriguez , Keane Dwight A. Sulit
Ultra-wideband (UWB) technology has become a key enabler for high accuracy indoor positioning systems (IPSs), where antenna performance directly influences localization accuracy, signal quality, and communication reliability. However, designing compact UWB microstrip patch antennas with wide bandwidth, low reflection loss, and stable radiation characteristics remains a significant challenge. This paper presents a PID-based performance optimization approach for UWB microstrip patch antennas to improve antenna characteristics for indoor positioning applications. The proposed methodology integrates MATLAB-based electromagnetic simulation with parameter optimization to refine antenna geometry while incorporating optimization concepts inspired by rough set theory and fuzzy logic to support efficient design parameter selection. Using MATLAB and the Parallel Computing Toolbox, the proposed approach significantly reduces computational complexity while accelerating the optimization process. Experimental results demonstrate substantial improvements in reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation pattern, and antenna directivity, particularly around the target operating frequency of 10 GHz. Among the evaluated configurations, the optimized rectangular microstrip patch antenna consistently outperformed the triangular design in terms of impedance matching and radiation performance. The proposed optimization framework provides an effective and computationally efficient solution for enhancing UWB antenna performance, making it well suited for high-precision indoor positioning and next-generation wireless communication systems.
Volume: 43
Issue: 2
Page: 450-459
Publish at: 2026-08-01

Low-power high-speed FinFET DRAM array using sleep transistors

10.11591/ijeecs.v43.i2.pp413-424
N Praveena , N Shylashree
Dynamic random-access memory (DRAM) is a fundamental memory technology widely employed in modern digital systems because of its high storage density and simple cell structure. However, conventional DRAM cells suffer from considerable power dissipation and propagation delay, which limit their suitability for high-speed and low-power applications. This paper presents three novel FinFET-based DRAM architectures incorporating sleep transistor techniques to reduce power consumption while improving operating speed. The proposed designs include a 2T-DRAM with sleep transistors and two configurations of 3T-DRAM with sleep transistors. The FinFET technology enhances switching performance and reduces propagation delay, whereas the sleep transistor technique effectively suppresses leakage, dynamic, and short-circuit power during memory operations. The proposed DRAM cells are designed and evaluated using the cadence virtuoso analog design environment. Simulation results demonstrate significant improvements over conventional DRAM architectures. The proposed 2T-DRAM achieves a 66% reduction in write delay, while the proposed 3T-DRAM achieves up to a 98% reduction in read delay. Furthermore, write power consumption is reduced by 56.8%, 63.02%, and 99.6% for the 2T, 3T-B, and 3T-C configurations, respectively. During read operations, power consumption is reduced by 99.8%, 99.5%, and 99.8%, respectively. These results demonstrate that the proposed FinFET DRAM architectures provide an effective solution for high-speed, low-power embedded memory applications.
Volume: 43
Issue: 2
Page: 413-424
Publish at: 2026-08-01

FireDetXplainer: an explainable artificial intelligence framework for wildfire detection

10.11591/ijeecs.v43.i2.pp576-585
Janjhyam Venkata Naga Ramesh , Bhargavi Peddi Reddy , Jillellamoodi Naga Madhuri Rajyalakshmi , Rajyalakshmi Uppada , Gaddam Venu Gopal , Rajesh Tulasi
Wildfires pose significant environmental, ecological, and socioeconomic threats, necessitating rapid and reliable detection systems for timely emergency response and disaster mitigation. Recent advances in deep learning have substantially improved wildfire detection accuracy; however, most existing models operate as black-box systems, limiting transparency and reducing user trust in safety-critical applications. This study proposes FireDetXplainer (FDX), an explainable artificial intelligence (XAI) framework designed to enhance the interpretability of deep learning-based wildfire detection while maintaining high predictive performance. The proposed framework integrates convolutional neural network (CNN)-based image classification with explainability techniques to identify the visual regions that contribute most to wildfire detection decisions. By generating intuitive visual explanations, FDX enables users to understand, validate, and trust the model's predictions, thereby supporting transparent and accountable decision-making. Experimental evaluation demonstrates that the proposed framework effectively distinguishes wildfire images from non-fire scenes while providing meaningful visual interpretations that improve model transparency without compromising detection performance. The findings highlight the potential of explainable AI to strengthen the reliability, usability, and practical deployment of intelligent wildfire monitoring systems for environmental surveillance, disaster management, and early warning applications.
Volume: 43
Issue: 2
Page: 576-585
Publish at: 2026-08-01

Context-aware AgriBot using dual intent and entity transformer and hybrid deep learning model

10.11591/ijai.v15.i4.pp3637-3645
Binod Deka , Ridip Dev Choudhury , Utpal Barman
The agriculture sector has gone through vast technological improvement, leading to increased productivity and sustainability. This research introduces AgriBot, a context-aware virtual assistant (VA) that helps farmers in rice cultivation and identifies diseases while providing instant suggestions for subsequent task. Using the RASA framework, AgriBot has been designed to understand the farmer's queries. Dual intent and entity transformer (DIET) classifier has been used for entity classification, achieving training and testing accuracy of up to 98% and 97%, respectively. Additionally, the system incorporates machine learning (ML) models for rice disease detection, utilizing a dataset of 4,624 images covering three major rice diseases: bacterial blight, brown spot, and blast. Among the tested models—neural network (NN), random forest (RF), support vector machine (SVM) and naive Bayes (NB) achieved an accuracy of up to 99.9%, demonstrating excellent classification performance. Using text-based query handling with image-based disease identification and instant suggestion makes it a more robust support system for the farmers.
Volume: 15
Issue: 4
Page: 3637-3645
Publish at: 2026-08-01
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