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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

A comparative study of Arabic morphological analyzers

10.11591/ijai.v15.i2.pp1876-1890
Omar Saadiyeh , Alaaeddine Ramadan , Chamseddine Zaki , Mohamad Hajjar , Gilles Bernard
The field of Arabic natural language processing (NLP) has witnessed significant advancements, driven by the development of various morphological analyzers. This paper compares several major Arabic morphological analyzers and examines their ability to handle word ambiguities, process dialects, operate efficiently, and support downstream NLP tasks. By reviewing previous studies, we identify key gaps, including the limited resources for dialects, the shortage of annotated corpora, and challenges related to system scalability. The study also highlights future directions, such as building larger and more diverse corpora, adapting neural models for dialects, and developing analyzers that are more interpretable and trustworthy. Overall, this comparative overview aims to provide a clearer understanding of the current state of Arabic morphological analyzers, synthesize existing research, and offer practical recommendations for future work in this area.
Volume: 15
Issue: 2
Page: 1876-1890
Publish at: 2026-04-01

A deep learning-based approach for hearing loss detection

10.11591/ijai.v15.i2.pp1701-1708
Deepa Deepa , Manjula Gururaj Rao
Millions of people across the world are affected by hearing loss and early detection is very important for effective intervention. The traditional hearing screening methods are effective but they often rely on specialized equipment and clinical resources, making them less accessible to common people. Hearing loss is a state that affects the ability to communicate, socially interact and overall quality of life. The advancements in recent years have aimed to enhance the accessibility and efficiency of hearing tests, mainly in remote areas. The accurate classification of hearing loss is essential for effective detection and treatment in audiology. This study presents a deep learning (DL)-based approach based on a feedforward neural network (FNN). This paper focuses on common causes like cerumen impaction, otitis media, and otosclerosis. The study tries to explore ways to improve the diagnosis of hearing loss. The goal is to develop solutions that make hearing screenings more accessible and cost-effective for populations with limited access to healthcare resources. The results show the advantages of DL models in supporting automated accurate classification of hearing loss for intelligent diagnostic systems in audiological healthcare.
Volume: 15
Issue: 2
Page: 1701-1708
Publish at: 2026-04-01

Hybrid kernel support vector machine with cuckoo search optimization for malaria detection from blood smear images

10.11591/ijai.v15.i2.pp1316-1326
Sri Huning Anwariningsih , Indrarini Dyah Irawati
Microscopic image-based malaria detection still struggles to capture complex features due to variations in lighting and color. The support vector machine (SVM) method is often used in medical image detection, but its performance depends heavily on the selection of optimal kernel and hyperparameters (C and gamma). Conventional approaches, with single kernels and manual tuning, have limitations in capturing both spatial information and color distribution simultaneously. Therefore, this research proposes hybrid kernel support vector machine-cuckoo search algorithm (HKSVM-CSA) method that combines the radial basis function (RBF) kernel and histogram intersection for SVM, along with hyperparameter optimization using the CSA. The dataset used is malaria cell images, which contains parasitized and uninfected images of blood cells. The proposed method comprises five main steps: dataset preparation, feature extraction, HKSVM, hyperparameter optimization, and model evaluation. Experiments demonstrate that the proposed model achieves 94% accuracy, 93% sensitivity, 94% specificity, and area under the curve (AUC) of 0.98, which is significantly better than standard SVM, SVM-genetic algorithm (GA), and k-nearest neighbors (KNN). These results show that combining kernel and CSA significantly improves detection accuracy. This approach is promising for image-based automatic systems for infectious disease diagnosis.
Volume: 15
Issue: 2
Page: 1316-1326
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

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

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

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

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

Recognition system based on artificial vision using OpenCV for discarding and detecting ceramics with defects

10.11591/ijai.v15.i2.pp1166-1173
Fernando Alvarado , Ricardo Yauri
Early detection of defects through preventive maintenance is important in industry to avoid economic losses, as in the case of ceramic tile manufacturing, where manual inspection allows defective parts to advance in production, causing delays. The research review shows that computer vision enables the automation of object detection, classification, and elimination tasks in industrial processes, using solutions based on Python, OpenCV, and MATLAB. For this reason, the design of a computer vision recognition system with OpenCV is proposed, which allows automatic discarding of ceramics with defects using an algorithm for detecting ceramics with a camera and Arduino-based hardware, comparing the captured images with a standard image on a conveyor belt. The machine vision system was integrated with a camera connected to a computer running OpenCV, achieving effective automatic detection with a threshold of 25% difference from the standard part. This percentage was calculated by comparing the grayscale pixel values with a reference image. The system calculates the proportion of pixels that exceed the similarity threshold. The conclusion is that the developed system contributes to production, highlighting the possibility of future industrial integration.
Volume: 15
Issue: 2
Page: 1166-1173
Publish at: 2026-04-01

Transformer-based Hindi image description and storytelling using enhanced attention and FastText embeddings

10.11591/ijai.v15.i2.pp1771-1782
Anjali Sharma , Mayank Aggrwal , Jitin Khanna
This work presents a novel image description generation framework that combines a Transformer-based encoder-decoder architecture with a custom squeeze-and-excitation (SE) attention block integrated into an EfficientNet feature extractor. The decoder uses FastText embeddings specifically trained for Hindi and is evaluated on the Microsoft common objects in context (MS-COCO) dataset. To improve the captioning process, the model incorporates a generative pre-trained transformer (GPT) module to generate narrative descriptions based on the initial captions and applies multiple similarity metrics to assess output quality. The proposed system significantly outperforms existing methods, achieving high bilingual evaluation understudy (BLEU) scores (BLEU-1 to BLEU-4: 83.24, 73.17, 64.56, and 58.22), a consensus-based image description evaluation (CIDEr) score of 81.41, an F1 score of 90.29, and a metric for evaluation of translation with explicit ordering (METEOR) score of 81.18, indicating strong caption accuracy. Furthermore, the model achieves low error rates, with a word error rate (WER) of 15% and a character error rate (CER) of 11%. This work highlights the challenges of applying large-scale datasets like MS-COCO to resource-limited languages and demonstrates the effectiveness of integrating FastText embeddings with transformer-based models for Hindi image captioning.
Volume: 15
Issue: 2
Page: 1771-1782
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

Session click sentiment behavior aware personalized recommendations system

10.11591/ijai.v15.i2.pp1539-1547
Suraj Bevinahalli Suresh , Padma Muthalambikashettahally Cheluvegowda
Session-based recommendations use short-term behavior of users to provide personalized suggestions to consumers in ecommerce platform. However, cold start, considering newly joined users and sparsity issues, where not enough short-term behavior is available, and the performance of traditional session-based recommendations is significantly impacted. Deep learning (DL) like recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and graph neural networks (GNNs) have been employed to capture session-clicks and enhance product recommendation accuracy. However, the current method is significantly affected due to the gradient descent problem in meeting convergence for top-K product recommendation. Further, the current method failed to capture product sentiment for session-clicks between inter-session and intra-session clicks. In addressing the research problems, the current research work introduced a session click sentiment behavior aware (SCSBA) personalized recommendation system using novel inter and intra session (IIS)-LSTM model. Finally, the objective function to recommend top K items to users is done using optimized Bayesian personalized ranking (OBPR) algorithm. Experiment outcome shows the SCSBA model achieves much better performance than state of art model, considering standard Tmall dataset.
Volume: 15
Issue: 2
Page: 1539-1547
Publish at: 2026-04-01

A sequential attention-enhanced deep learning framework for robust potato leaf disease diagnosis under real field conditions

10.11591/ijai.v15.i2.pp1790-1803
Watcharkorn Yoochomboon , Nithizethe Mhuadthongon , Piyaporn Krachodnok
Diagnosing potato leaf diseases from images collected in real-life field settings is challenging, mainly because of uneven lighting, complex backgrounds, and disease symptoms that are often subtle or visually inconsistent. In this study, a deep learning-based framework was developed to support potato leaf disease diagnosis, with particular attention given to improving generalization and interpretation. Several convolutional neural network (CNN) architectures were first examined under the same experimental conditions, and ResNeXt-50 showed the most stable overall performance. The model was then extended by applying efficient channel attention (ECA), followed by spatial attention adapted from the convolutional block attention module (CBAM). Test results indicate that this sequential attention design performs better than the baseline model as well as variants using only a single attention mechanism. Additional evaluation using 300 real-field images collected under different field conditions suggests improved robustness, while visualization results from gradient weighted class activation mapping (Grad-CAM) show clearer focus on lesion-related regions. Overall, the findings suggest that combining channel wise and spatial attention can improve both prediction reliability and interpretability, making the approach suitable for practical agricultural use.
Volume: 15
Issue: 2
Page: 1790-1803
Publish at: 2026-04-01

Evaluating hybrid and standard deep learning models for maximum temperature forecasting in a semi-arid region

10.11591/ijeecs.v42.i1.pp183-193
Oussama Zemnazi , Sanaa El Filali , Sara Ouahabi , Abderrahim Mouhtadi
Temperature forecasting is important for industries affected by climate, especially in semi-arid regions where the weather can change quickly and is hard to predict over time. Many studies have examined various deep learning models, including long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNNs), and transformer-based hybrids. However, their performance in data-limited semi-arid environments is often unclear and inconsistent. This study compares six deep learning methods for predicting daily maximum temperatures in Settat, Morocco. It uses 11 years of ground-observed meteorological data. The models examined include a baseline artificial neural network (ANN) and five hybrid structures: ANN-LSTM, ANN-GRU, ANN-CNN, ANN–random forest (RF), and ANN-transformer. The results indicate that the ANN performs the best overall, with MAE = 0.0432, root mean square error (RMSE) = 0.0543, and R² = 0.8820. It surpasses all hybrid models. When using a relative improvement metric, the ANN shows accuracy gains of 32% to 42% compared to the recurrent, convolutional, and attention-based hybrids. These results suggest that in semi-arid climates, where maximum temperature mainly depends on the same-day atmospheric conditions, simpler feedforward models work better than more complex temporal models. The study underscores the need to match model complexity with climatic factors and dataset size, offering a useful benchmark for temperature forecasting in regions with limited data.
Volume: 42
Issue: 1
Page: 183-193
Publish at: 2026-04-01
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