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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 1,893 Documents
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Revolutionising essay writing: a systematic review of Google Gemini Jen, Shirley Ling; Salam, Abdul Rahim; Mat, Hamidah; Wei Lun, Wong
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1839-1850

Abstract

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.
Efficient text detection and recognition in natural scene images using novel blended ensemble deep learning Reddy Patil, Rajeswari; Dammergidda, Aradhana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1664-1679

Abstract

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.
Unimodal and multimodal techniques for depression diagnosis: a comprehensive survey Jayasree, Swathy; Sridhar, Yashawini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1947-1954

Abstract

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.
Analysis of tuberculosis detection using deep learning technique and explainable artificial intelligence Srinivas, Shashikiran; Patil, Kavita Avinash; Monappa Rama, Kushalatha; Venkateshlu, Sudha; Muthuswamy, Jayanthi; Babu Narayanappa, Srinivas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1623-1631

Abstract

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.
Energy-efficient virtual machine allocation using directional and boundary-aware bobcat optimization Gouse, Nida Kousar; Chandrasekaran, Gopala Krishnan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1286-1299

Abstract

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).
Gradient-based stochastic depth with convolutional neural network for coconut tree leaf disease classification Gopalakrishna, Kavitha Magadi; Lingaraju, Raviprakash Madenur; Jayachandra, Ananda Babu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1155-1165

Abstract

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.
Summarization of IndoSum dataset using enhanced TextRank with weighted word embedding Yulianti, Evi; Umbara, Piawai Said
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1919-1930

Abstract

This study evaluates the effectiveness of combining the TextRank method with word embedding on the Indonesian text summarization (IndoSum) dataset. Two experimental scenarios were applied: unweighted and weighted. The unweighted scenario incorporates word embedding, such as Word2Vec, FastText, and Indonesian bidirectional encoder representations from transformers (IndoBERT), into the TextRank framework. The weighted scenario further augments the term frequency-inverse document frequency (TF-IDF) weighting to the word embedding in the initial scenario. Our results on the effectiveness of enhanced TextRank using word embedding on IndoSum data are consistent with those reported in previous work on Liputan6 data. Both scenarios can significantly improve the effectiveness of TextRank summarization. Then, the weighted scenario showed performance improvement in most summarization systems compared to the unweighted scenario, with an average performance increase of 5.55% in recall-oriented understudy for gisting evaluation (ROUGE)-1 and 9.95% in ROUGE-2. This result confirms the robustness of the enhanced TextRank with weighted word embedding on the IndoSum data. Lastly, our study also highlights the importance of using domain-specific training data to optimize summarization performance.
Energy-efficient and secure WSN clustering for IoT using particle swarm optimization and advanced encryption standard Kumar, S. Swapna; Satyanarayan Reddy, Kalli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1275-1285

Abstract

Wireless sensor networks (WSNs) are made up of distributed sensor nodes that work together under energy and communication constraints. They support diverse internet of things (IoT) applications such as smart agriculture and environmental monitoring. This paper proposes a technique to optimize the WSN framework for secure and energy-efficient data transmission. To improve cluster formation and network energy consumption, the suggested model combines k-means clustering with particle swarm optimization (PSO). Inter-cluster data is encrypted by the cluster head (CH) using the advanced encryption standard (AES)-128. To protect data and save energy, the low-energy adaptive clustering hierarchy (LEACH) protocol uses a number of techniques. Energy efficiency, model accuracy, likelihood of privacy breaches, and network longevity are examples of performance metrics. The system is tested by Python simulations on the Intel Berkeley Research Lab (IBRL) real-world dataset, which includes 54 sensor nodes measuring temperature and humidity. The results demonstrate significant energy savings and a model accuracy of 96.50%, thereby reducing privacy breaches and extending network lifetime. The framework offers scalability, effective privacy monitoring, and adaptability to changing topologies.
A comparative study of Arabic morphological analyzers Saadiyeh, Omar; Ramadan, Alaaeddine; Zaki, Chamseddine; Hajjar, Mohamad; Bernard, Gilles
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1876-1890

Abstract

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.
A deep learning-based approach for hearing loss detection Deepa, Deepa; Rao, Manjula Gururaj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1701-1708

Abstract

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.

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