IAES International Journal of Artificial Intelligence (IJ-AI)
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.
Articles
85 Documents
Search results for
, issue
"Vol 15, No 2: April 2026"
:
85 Documents
clear
<![CDATA[Development of rough set based machine learning approach to screen breast cancer ]]>
<![CDATA[Sivakumar, Sangeetha]]>;
<![CDATA[Sathish, Shakeela]]>;
<![CDATA[Datta, Debabrata]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1982-1998
<![CDATA[One of the major causes of death for women is breast cancer. A substantial number of women diagnosed with breast cancer die due to inaccuracies in diagnosis and delays in treatment. Cancer prediction must be accurate in order to improve treatment quality and patient survival rates. This study evaluates logistic regression (LR), decision tree algorithm (DTA), and adaptive boosting (AdaBoost) (AB ensemble learning algorithm) in conjunction with rough set theory (RST) to enhance breast cancer classification using the Wisconsin diagnosis breast cancer dataset (WDBC). By employing rough set approximations, including the upper and lower bounds of features, this study introduces a novel rough AdaBoost (Rough AB) algorithm to improve classification accuracy. Various performance indices are compared across algorithms. The proposed Rough AB algorithm demonstrated superior performance, particularly in prediction accuracy for both benign and malignant cases. It incorporates roughness to determine the starting node of the decision stump, offering a significant improvement in ensemble learning techniques for medical diagnostics. It gives practical implications for clinical decision-making, potentially enabling more reliable and timely breast cancer diagnoses, which can significantly impact patient outcomes. The proposed method leverages rough set approximations to refine feature selection and improve prediction accuracy. Also, it positions RST as an explainable artificial intelligence (XAI) technique, highlighting its interpretability, ethical transparency, and potential integration with deep learning for clinical deployment.]]>
<![CDATA[Intelligent self-organizing microservice composition using hybrid learning for neonatal ward ]]>
<![CDATA[Poornima, Sharon]]>;
<![CDATA[Immanuel V, Ashok]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1097-1108
<![CDATA[This research presents an innovative self-organizing microservice composition model specifically tailored for dynamic and time-sensitive healthcare environments such as Neonatal Intensive Care Units(NICU). A hybrid machine learning classifier detects neonatal conditions and assigns treatment plans based on real-time vitals. The composition process is guided by a deep learning agent that combines unsupervised and reinforcement learning to develop intelligent bonding strategies. Microservices act as autonomous agents, supporting decentralised service choreography within the self-organizing framework. The bonding strategies of direct bonding and shared bonding are implemented for single conditions and coexisting conditions, respectively. The simulation results are based on actual NICU data, demonstrating the ability of the model to dynamically compose services while ensuring optimal resource utilisation. The model demonstrates an adaptive and dynamic composition through emergence and continuous learning for changing clinical conditions, and demonstrates emergent behaviour through reinforcement learning. The model’s predictive capabilities enable anticipatory service loading, providing context-aware treatment in critical healthcare scenarios. This self-organizing architecture model offers a scalable and robust solution for autonomous, decentralised service choreography in critical healthcare environments.]]>
<![CDATA[Revolutionising essay writing: a systematic review of Google Gemini ]]>
<![CDATA[Jen, Shirley Ling]]>;
<![CDATA[Salam, Abdul Rahim]]>;
<![CDATA[Mat, Hamidah]]>;
<![CDATA[Wei Lun, Wong]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1839-1850
<![CDATA[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.]]>
<![CDATA[Efficient text detection and recognition in natural scene images using novel blended ensemble deep learning ]]>
<![CDATA[Reddy Patil, Rajeswari]]>;
<![CDATA[Dammergidda, Aradhana]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1664-1679
<![CDATA[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.]]>
<![CDATA[Unimodal and multimodal techniques for depression diagnosis: a comprehensive survey ]]>
<![CDATA[Jayasree, Swathy]]>;
<![CDATA[Sridhar, Yashawini]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1947-1954
<![CDATA[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.]]>
<![CDATA[Analysis of tuberculosis detection using deep learning technique and explainable artificial intelligence ]]>
<![CDATA[Srinivas, Shashikiran]]>;
<![CDATA[Patil, Kavita Avinash]]>;
<![CDATA[Monappa Rama, Kushalatha]]>;
<![CDATA[Venkateshlu, Sudha]]>;
<![CDATA[Muthuswamy, Jayanthi]]>;
<![CDATA[Babu Narayanappa, Srinivas]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1623-1631
<![CDATA[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.]]>
<![CDATA[Energy-efficient virtual machine allocation using directional and boundary-aware bobcat optimization ]]>
<![CDATA[Gouse, Nida Kousar]]>;
<![CDATA[Chandrasekaran, Gopala Krishnan]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1286-1299
<![CDATA[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).]]>
<![CDATA[Gradient-based stochastic depth with convolutional neural network for coconut tree leaf disease classification ]]>
<![CDATA[Gopalakrishna, Kavitha Magadi]]>;
<![CDATA[Lingaraju, Raviprakash Madenur]]>;
<![CDATA[Jayachandra, Ananda Babu]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1155-1165
<![CDATA[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.]]>
<![CDATA[Summarization of IndoSum dataset using enhanced TextRank with weighted word embedding ]]>
<![CDATA[Yulianti, Evi]]>;
<![CDATA[Umbara, Piawai Said]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1919-1930
<![CDATA[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.]]>
<![CDATA[Energy-efficient and secure WSN clustering for IoT using particle swarm optimization and advanced encryption standard ]]>
<![CDATA[Kumar, S. Swapna]]>;
<![CDATA[Satyanarayan Reddy, Kalli]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v15.i2.pp1275-1285
<![CDATA[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.]]>
