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30,938 Article Results

Double-hop of reconfigurable intelligent surfaces-aided for wireless optical link under log-normal fading channels

10.11591/ijai.v15.i2.pp1174-1180
Duong Huu Ai , Van Loi Nguyen , Khanh Ty Luong
In optical wireless communication (OWC), the reconfigurable intelligent surfaces (RIS) are used to manipulate optical signals by controlling the phase shifts or amplitude of reflected beams, which helps improve signal quality. RIS units can be tailored to increase the strength and reliability of the communication link, especially in challenging fading conditions. The double-hop scenario involves two RIS-assisted segments, such as transmitter to RIS-1 and RIS-1 to RIS-2 or a receiver. Each hop encounters log-normal fading, which impacts the overall link performance. Log-normal fading models the irradiance fluctuation caused by turbulence, which is significant in free-space optical (FSO) systems, this fading model assumes that the received optical signal’s amplitude varies with a log-normal distribution, making it more suited for weak to moderate turbulence. Numerical results are obtained under different of link distance, subcarrier quadrature amplitude modulation (QAM) is displayed quantitatively illustrate the average symbol error rate in the absence of RIS and with double-hop of RIS.
Volume: 15
Issue: 2
Page: 1174-1180
Publish at: 2026-04-01

AI-induced fatigue among students in higher education: a latent profile analysis

10.11591/ijai.v15.i2.pp1963-1971
Dynah D. Soriano , Jordan L. Salenga , John Paul P. Miranda , Juvy C. Grume , Emerson Q. Fernando , Jr., Amado B. Martinez , Raymond A. Cabrera , Jaymark A. Yambao
The integration of artificial intelligence (AI) tools in education offers significant benefits but also introduces challenges, including AI-induced fatigue among students. This study aimed to classify students’ experiences with AI tools using latent profile analysis (LPA). A quantitative cross sectional design and referral approach were used to collect survey data from 388 college students who actively used AI tools for academic purposes from November to December 2024. The survey measured AI usage intensity, AI literacy, self-efficacy, perceived usefulness, cognitive load, technostress, sleep quality, general fatigue levels, and attitude toward AI. Descriptive results indicated moderate levels of AI usage intensity, AI literacy, perceived usefulness, cognitive load, sleep quality, and general fatigue, with technostress and attitude toward AI also at moderate levels. Model selection considered Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and profile size adequacy, and expert review supported the retained six-profile structure. The LPA identified six interpretable user groups: competent but sleep-deprived users, overwhelmed and high-strain users, stable moderate users, strained moderate users, high intensity strained users, and low-strain selective users. The findings show differences in patterns of competence, strain, fatigue, and sleep outcomes associated with AI tool use, which supports the development of profile specific strategies to manage technostress, cognitive load, fatigue, and sleep disruption among higher education students.
Volume: 15
Issue: 2
Page: 1963-1971
Publish at: 2026-04-01

Real-time detection of rider fatigue: a comparative study of black-box and glass-box artificial intelligence approaches

10.11591/ijai.v15.i2.pp1409-1417
Cynthia Hayat , Iwan Aang Soenandi , Budi Harsono
Rider fatigue poses a critical safety challenge in two-wheeled vehicle operation due to limited physical protection, increased balance demands, and prolonged exposure to environmental stressors. Effective real-time fatigue detection is essential to mitigate accident risks, particularly in high-traffic regions such as Indonesia. This study presents a comparative analysis of black-box and glass-box artificial intelligence (AI) models for real-time detection of rider fatigue, evaluated through a human factor’s lens emphasizing interpretability, intrusiveness, and cognitive compatibility. Multimodal data comprising physiological signals, behavioral indicators, and environmental context were collected using wearable sensors and rider telemetry to train and assess the models. Experimental results reveal that black-box models, including convolutional neural network (CNN) + long short-term memory (LSTM), random forest (RF), and support vector machine (SVM), achieve superior predictive accuracy (94.3%, 91.5%, and 88.2%, respectively) but lack inherent transparency. Conversely, glass-box models such as decision tree (DT) and logistic regression (LR) offer greater interpretability, a critical factor in safety-sensitive applications, though with reduced accuracy (approximately 83–85%). These findings underscore the trade-off between predictive performance and explainability, highlighting the need to tailor model choice to specific operational requirements. This research advances the design of intelligent, human-centered rider support systems that balance accuracy, transparency, and user trust, fostering safer two-wheeled transportation.
Volume: 15
Issue: 2
Page: 1409-1417
Publish at: 2026-04-01

2D-CNN-GACL-ECGNet graph attention: a robust framework for electrocardiogram-based stress detection

10.11591/ijai.v15.i2.pp1529-1538
P. Kavitha , L. Shakkeera
Early detection of cardiovascular diseases (CVDs) via electrocardiogram (ECG) classification during physiological stress is critical and remains challenging due to stress-induced morphological variability, noise from ambulatory settings, and inter-class ambiguities. Existing models, such as 1D signal-based models with convolutional neural networks (CNNs) and graph convolutional networks (GCNs), struggle to adapt to dynamic stress conditions and generate interpretable insights. In response, we propose 2D CNN and graph attention network (GAT) for optimizer. The model 2D-CNN and GACL-ECG-Net, an innovative framework integrating GATs with adaptive contrastive learning (ACL) and morpho-temporal graph construction. Key innovations include 2D-CNN denoising, 2D transformation, dynamic morpho-temporal graphs modeling ECG beats as nodes with hybrid edges (70% morphological similarity, 30% temporal proximity), and stress-adaptive contrastive loss with learnable margins on stress-conditioned labels, reducing class ambiguity by 18%. Multi-head attention mechanisms provide interpretable heatmaps aligned with cardiologist annotations (κ =0.82) and are evaluated using Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, wearable stress and affect detection (WESAD) dataset for emotional stress, and stress at work, knowledge work (SWELL-KW) dataset for cognitive stress. 2D-CNN-GACL-ECG-Net achieves state-of-the-art performance with 98.7% F1-score (MIT-BIH), 94.2% (WESAD), and 92.8% (SWELL-KW), outperforming CNN-bidirectional long short-term memory (BiLSTM) and GCN baselines by 95%. The framework is computationally efficient and clinically validated for wearable health monitoring.
Volume: 15
Issue: 2
Page: 1529-1538
Publish at: 2026-04-01

Research themes and trends in the field of blockchain engineering: a topic modelling analysis

10.11591/ijai.v15.i2.pp1863-1875
Dinara Zhaisanova , Madina Mansurova
This study employed topic modeling to identify key research themes in blockchain engineering and examined how these themes have evolved over time. The dataset of collected abstracts from 3,665 relevant papers of Web of Science (WoS) core collection for the period from 2019 to 2024 was analyzed with latent Dirichlet allocation (LDA) approach. Based on the results of the topic development trends analysis, the topics collectively highlight the evolving landscape of technologies such as blockchain, smart contracts, the internet of things (IoT), and edge computing, focusing on their integration and impact across sectors like finance, healthcare, supply chain management, and energy systems. It offers valuable insights and implications for research related to blockchain engineering. Latent semantic indexing (LSI) provided further understanding by highlighting strong connections between specific topics, such as energy trading, supply chains, and medical applications. A comparison of LDA and LSI topics revealed overlapping themes, which supports the reliability of the topic structure identified by LDA.
Volume: 15
Issue: 2
Page: 1863-1875
Publish at: 2026-04-01

A reinforcement-guided multi-phase hybrid architecture for threat profiling and defense towards IoT handheld device

10.11591/ijai.v15.i2.pp1497-1504
Pushpa Rajput Narayana Singh , Neelambike Siddalingaiah
The contribution of artificial intelligence (AI) towards offering proactive security in handheld devices of internet of things (IoT) is in evolving stage. Review of literature showcases noteworthy attempts of machine learning (ML) and deep learning (DL) models; however, they are a large scope of improvement towards bridging the trade-off between security and computational-communication efficiency. This problem is addressed in this manuscript by presenting a unique and innovative solution where reinforcement learning (RL) has been hybridized with standalone ML and DL models. The model reads the permission-based data in cloud, followed by vulnerability prediction carried out by hybridization of RL and logistic regression (LR). Further, RL is integrated with deep neural network (DNN) for exploring a secure path to facilitate data transmission. The proposed model witnessed 97.9% accuracy, 67.35% of higher accuracy, 55.14% of reduced latency, and 52.54% of faster response time in contrast to baselines.
Volume: 15
Issue: 2
Page: 1497-1504
Publish at: 2026-04-01

Exponential long short-term memory with Levy flight optimization for lung nodule classification

10.11591/ijai.v15.i2.pp1451-1463
Kaliba Gowthami , Kamalakannan Jayaseelan
Lung cancer, which commonly appears as lung nodules is a deadly type of cancer that develops in a lung. Early detection of lung cancer is critical and challenging task due to presence of overlapping structures, which make it challenging to differentiate the benign and malignant regions. This research proposes long short-term memory (LSTM) with exponential linear unit (ELU) method for the classification of different classes of lung nodules. The hyperparameters of the LSTM network are optimized using the developed dynamic Levy flight – Archimedes optimization algorithm (DLF-AOA), which effectively identifies the optimal parameters for classification. The ResNet-18 method is used for the extraction of high-level features to differentiate various classes of lung nodules. Furthermore, Bayesian active contour (BAC) is employed for the segmentation of images as containing cancerous and non-cancerous regions of lung nodules. The LSTM with ELU method achieves 98.56% accuracy, 97.54% sensitivity, 98.22% specificity, 96.93% precision, 96.33% F1-score, and 1.44 error rate in IQ-OTH/NCCD lung cancer dataset.
Volume: 15
Issue: 2
Page: 1451-1463
Publish at: 2026-04-01

Analysis of tuberculosis detection using deep learning technique and explainable artificial intelligence

10.11591/ijai.v15.i2.pp1623-1631
Shashikiran Srinivas , Kavita Avinash Patil , Kushalatha Monappa Rama , Sudha Venkateshlu , Jayanthi Muthuswamy , Srinivas Babu Narayanappa
Tuberculosis (TB) affects the health of many individuals and is still a prime worldwide health concern despite having so many advanced treatments, as it still lacks technical advancement in its treatment and diagnosis. Accuracy in identification and early detection is essential to reduce the spread and improve treatment outcomes. Traditional methods of diagnosis, such as sputum microscopy and culture, are labor-dependent and subject to human mistakes as it is done by lab technicians. Recent improvements in deep learning have demonstrated significant potential for enhancing and automating diagnostic accuracy. Our research proposes a deep learning based technique that detects TB from chest X-rays after image processing techniques like augmentation. After training on big data, our model pulls off an astonishing accuracy of 97.42% and a loss of 7.17%, outperforming traditional methods. The model uses convolutional neural network (CNN) as a base and transfer learning method, like DenseNet-121, and explainable artificial intelligence (XAI) technique, like Grad-CAM, to recognize TB related patterns effectively and with low false positives. This approach has the ability to revolutionize the diagnosis of TB and offer more dependable, scalable, and timely solutions to healthcare systems worldwide.
Volume: 15
Issue: 2
Page: 1623-1631
Publish at: 2026-04-01

Energy-efficient virtual machine allocation using directional and boundary-aware bobcat optimization

10.11591/ijai.v15.i2.pp1286-1299
Nida Kousar Gouse , Gopala Krishnan Chandrasekaran
Cloud computing (CC) has gained significant traction due to its ability to deliver services in a scalable and adaptable manner, catering to diverse user requirements. However, in virtualization technology, one of the primary challenges is managing the energy consumption required to maintain service quality, as it directly impacts the operational expenses of data centers. To address this challenge, this research proposes a directional movement and boundary-aware strategy-based bobcat optimization algorithm (DMBABOA) for energy-efficient virtual machine (VM) allocation aimed at minimizing energy consumption in cloud environments. The directional search and boundary-aware correction enhance convergence and ensure feasible resource distribution. This ensures effective utilization of resources, improved virtualization management, and substantial energy savings. The experimental findings establish that the proposed DMBABOA optimizer reaches a minimum execution time of 134.48 s when the number of VMs is equal to 1,200 with 200 users, compared to existing methods such as the metaheuristic VM allocation approach to power efficiency of sustainable cloud environment (MV-PESC).
Volume: 15
Issue: 2
Page: 1286-1299
Publish at: 2026-04-01

Unimodal and multimodal techniques for depression diagnosis: a comprehensive survey

10.11591/ijai.v15.i2.pp1947-1954
Swathy Jayasree , Yashawini Sridhar
Depression is a common and major mental health condition that affects individuals across all age groups and any backgrounds, severely reducing their physical, emotional, and cognitive functioning. It goes beyond typical mood swings and requires a timely and accurate diagnosis to prevent severe consequences such as suicidal tendencies, self-harm, and long-term mental decline. The improving performance of deep learning and machine learning techniques has significantly enhanced the speed and accuracy of depression diagnosis using both unimodal and multimodal features. This comprehensive study gives a complete overview of the unimodal and multimodal methods used to diagnose depression in its early stages. Additionally, this survey summarizes the dataset, methods, and limitations of previous work presented in the domain of depression diagnosis and serves as a suitable reference for future analysis.
Volume: 15
Issue: 2
Page: 1947-1954
Publish at: 2026-04-01

Revolutionising essay writing: a systematic review of Google Gemini

10.11591/ijai.v15.i2.pp1839-1850
Shirley Ling Jen , Abdul Rahim Salam , Hamidah Mat , Wong Wei Lun
The emergence of generative artificial intelligence (GenAI) has significantly impacted the education sector in essay writing. This study focuses on Google Gemini as a viable alternative to ChatGPT. A systematic literature review (SLR) was conducted using preferred reporting items for systematic reviews and meta-analyses (PRISMA) method to investigate existing research on Gemini and its application in essay writing. The review examined articles published from 2022 to August 2024. It focuses on the years, research design, population, and learning theories involved in the use of Gemini. Several stages of the PRISMA method were implemented to filter and collect relevant information, resulting in a comprehensive analysis of articles discussing Gemini’s role in essay writing across various publication platforms. The findings highlight the functions of Gemini in essay writing. It provides valuable insights for researchers and practitioners in language teaching and learning. This research aims to enhance understanding and promote the effective use of Google Gemini in education.
Volume: 15
Issue: 2
Page: 1839-1850
Publish at: 2026-04-01

Gradient-based stochastic depth with convolutional neural network for coconut tree leaf disease classification

10.11591/ijai.v15.i2.pp1155-1165
Kavitha Magadi Gopalakrishna , Raviprakash Madenur Lingaraju , Ananda Babu Jayachandra
The coconut palm (Cocos nucifera) is vital plantation crop, valued for their different uses, ranging from their fruit to its trunk. In recent times, it has been observed that many coconut trees are affected by diseases that reduce production and weaken the strength of the coconut. The classification of coconut leaf diseases is challenging because of intra-class and inter-class variability. This research introduces the gradient-based stochastic depth (GSD) with convolutional neural network (CNN) technique to coconut leaf disease classification to overcome these challenges. The GSD technique is incorporated into every layer of the CNN, where it calculates the probability using gradient magnitudes and skips layers that contribute minimally to the classification. The images are segmented using the GrabCut segmentation algorithm, which isolates the leaf from the background using graph-based segmentation, helping to differentiate between various disease classes. The GSD with CNN algorithm obtains an accuracy of 96.42%, precision of 96.15%, recall of 95.87%, and F1-score of 95.93%, while comparing with existing algorithms.
Volume: 15
Issue: 2
Page: 1155-1165
Publish at: 2026-04-01

Efficient text detection and recognition in natural scene images using novel blended ensemble deep learning

10.11591/ijai.v15.i2.pp1664-1679
Rajeswari Reddy Patil , Aradhana Dammergidda
Text detection and recognition in natural scene images is a critical task in computer vision, with applications ranging from document analysis to autonomous navigation. This work presents a robust and efficient pipeline that integrates YOLOv8 for text detection and EasyOCR for recognition, enhanced by an adaptive preprocessing mechanism between the two stages. The YOLOv8 model is trained on a custom dataset with polygonal annotations converted into YOLO format ensures precise bounding box formations around the text regions. An adaptive preprocessing module dynamically optimizes the detected regions adjusting resolution, noise reduction, and orientation before passing them to EasyOCR, significantly improving robustness. The lightweight yet powerful EasyOCR engine then recognizes text across diverse fonts, styles, and orientations. Evaluated on the benchmark Total-Text dataset, the proposed method demonstrates superior performance in detection accuracy, recognition precision, and computational efficiency. Additionally, this work provides a detailed analysis of training metrics, to validate the model’s robustness. The proposed system is scalable and can be integrated into real-time applications such as license plate recognition, document digitization, and assistive technologies for the visually impaired.
Volume: 15
Issue: 2
Page: 1664-1679
Publish at: 2026-04-01

Genetic algorithm for generalized time-window assignment problem

10.11591/ijai.v15.i2.pp1261-1274
Ali Kansou , Bilal Kanso , Houssein Wehbe , Haydar Bazzi , Ali Mcheik
This paper presents a hybrid genetic algorithm (GA) for the generalized time-window assignment problem (GTWAP), a complex artificial intelligence (AI) scheduling challenge that involves assigning agents to resources under strict temporal and capacity constraints. Our method integrates a problem specific heuristics and a repair mechanism to generate feasible and high quality solutions. We provide a mathematical formulation for GTWAP and introduce a new public benchmark set, using CPLEX to obtain exact solutions. Computational experiments demonstrate that our GA is highly competitive with CPLEX, often matching its performance. This effectiveness makes our method a practical and scalable AI-driven tool for complex scheduling in domains like logistics and healthcare.
Volume: 15
Issue: 2
Page: 1261-1274
Publish at: 2026-04-01

Semantic-syntactic graph network for aspect-based sentiment analysis

10.11591/ijai.v15.i2.pp1814-1824
Rekha Bdurga Harish , Neelambike Siddalingaiah
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that identifies sentiment polarities toward specific aspects within a sentence. While conventional models have achieved progress, they often neglect to jointly consider both semantic context and syntactic structure, limiting performance in complex linguistic scenarios. Nevertheless, most existing graph convolutional network (GCN)-based approaches have recently focused on either semantic or syntactic information individually, leading to suboptimal sentiment classification accuracy. Hence, this work aims to design an effective ABSA model that simultaneously captures both semantic relationships and syntactic dependencies for enhanced aspect-level sentiment analysis. For solving issues of GCN-based approaches, this work proposed a model called sentiment semantic syntactic network (SentSemSynNet), which constructs a unified graph by integrating semantic and syntactic features and applies graph neural networks to learn rich, aspect-specific representations. The model was evaluated on the SemEval2014 restaurant and laptop datasets. It achieved 88.25% accuracy and 82.95% macro-F-score for restaurant, and 84.52% accuracy and 80.26% macro-F-score for laptop. The model’s unique integration of both semantic and syntactic importance through a unified graph structure improved sentiment detection accuracy.
Volume: 15
Issue: 2
Page: 1814-1824
Publish at: 2026-04-01
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