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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
 186   187   188   189   190 
Explainable social media disaster image classification using a lightweight attention-based deep learning approach Kangokar Taranath, Rashmi; Chidanandappa Mara, Geeta
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.pp1464-1472

Abstract

In recent years, the rapid dissemination of social media content during natural and man-made disasters has created a need for automated and accurate disaster image classification systems. This paper proposes lightweight explainable attention-based disaster network (LEAD-Net), a deep learning (DL) model designed for classifying disaster-related images with high accuracy and interpretability. The system integrates an EfficientNet-B0 backbone enhanced with squeeze-and-excitation (SE) attention modules and a lightweight neural architecture search (NAS-lite) strategy for tuning the classifier head and training hyperparameters. The model was evaluated on two benchmark datasets comprehensive disaster dataset (CDD) and damage multimodal dataset (DMD) achieving 96% and 87% accuracy, respectively, outperforming several established convolutional neural network (CNN) baselines. To ensure transparency, gradient-weighted class activation mapping (Grad-CAM) was employed to generate visual explanations of the model’s decisions, confirming its focus on semantically relevant image regions.
Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology Moozhippurath, Bineesh; Natarajan, Jayapandian
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.pp1605-1612

Abstract

Effective treatment of lung cancer depends on early and accurate detection, which continues to be a major cause of cancer-related fatalities globally. Conventional diagnostic techniques are useful, but their efficacy in handling large amounts of thoracic computed tomography (CT) scan data is limited by their time-consuming nature and susceptibility to human error. The research here suggests a new deep learning model that integrates generative adversarial networks (GANs) for data improvement with a sophisticated ensemble approach to classification. GANs are employed to generate realistic synthetic CT images, addressing the challenges of limited datasets. The backbone of the proposed approach is a consensus-guided adaptive blending (CGAB) ensemble model that learns to dynamically combine the predictions of three top-performing convolutional neural networks (CNNs): ResNet-152, DenseNet-169, and EfficientNet-B7. The CGAB model improves prediction accuracy through model contribution weighting based on confidence scores and inter-model consensus, while a conflict-resolving auxiliary decision model is used. The approach was tested using the lung image database consortium and the image database resource initiative (LIDC-IDRI) dataset with a detection rate of 97.35%, surpassing single model and traditional ensemble methods. The current work provides a solid and scalable approach to lung cancer detection with better generalization, increased diagnostic consistency, and applicability for clinical use.
Genetic algorithm-based chicken manure weight prediction system development Hudaya, Rida; Wirayoga, Septriandi; Sarosa, Moechammad; Yusuf, Muhammad; Prayugo, Armanda Dwi
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.pp1247-1260

Abstract

This research presents design and implementation of internet of things (IoT) based monitoring and predictive system for evaluating chicken manure weight and environmental conditions in poultry housing. The proposed system integrates MQ-137 sensor for ammonia detection, DHT22 sensor for temperature and humidity measurement, and load cell modules for manure weight monitoring. All sensor data are transmitted in real time to cloud platform, enabling continuous environmental assessment. A 30-day experimental study was conducted using two controlled chicken drum models, each containing 15 broiler chickens and provided with different feed types to observe variations in manure production and air quality. Sensor calibration results indicate high accuracy, with average error of 0.31% for ammonia readings and 0.10% for manure weight measurement. Experimental findings show that feed type A generates lower manure weight, reduced ammonia concentration, and more stable temperature conditions compared to feed type B, suggesting improved feed efficiency and better overall chicken health. A genetic algorithm (GA) was employed to optimize regression model predicting manure weight using ammonia concentration and temperature as input features. The GA-optimized model achieved strong predictive performance, with root mean square error (RMSE) of 0.358 g and coefficient of determination (R2) value of 0.992. The results demonstrate that proposed system provides reliable, scalable, and data-driven solution for smart poultry monitoring and early health detection.
Knowledge graph-based enhanced virtual network embedding for 6G cloud datacenter deployment Abdelrahim, Shourok; Ghoniemy, Samy; Aborizka, Mohamed
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.pp1181-1193

Abstract

Virtual network embedding (VNE) is the effective mapping of virtual networks onto shared physical substrate networks while boosting resource utilization and ensuring quality of service (QoS). VNE is a real challenge in network virtualization, especially in the perspective of 6G-enabled datacenters, where the demand for ultra-low latency, heavy connectivity, and dynamic resource allocation is vital. The proposed solution enables the ability to infer indirect paths for the resources prediction task on the knowledge graph (KG) by making implicit meaningful relations among the entities that compose the resource network. The simulation results indicated the inference mechanism significantly improves efficiency and adaptability. This leads to overall performance gains in terms of runtime stability, resource utilization, and energy savings in dynamic 6G scenarios. The experimental results showed that the proposed solution provided a 24.9% reduction in energy consumption for small-sized virtual network requests (VNRs), while maintaining 24.8% and 23.9% for medium and large VNRs, respectively, while it significantly decreased the delay time compared to the resulted delay using the baseline models such as asynchronous advantage actor-critic (A3C) + graph convolutional network (GCN). The results also confirmed that the integration of the inference engine algorithm with the embedding process results in remarkable reduction in the execution time while preserving embedding accuracy.
Deep learning for mental health analysis: long short-term memory approach to text-based condition classification Yamani, Zaqqi; Lestarini, Dinda; Raflesia, Sarifah Putri; Sari, Purwita; Athalina, Ghita
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.pp1762-1770

Abstract

The increasing prevalence of mental health disorders highlights the need for scalable and automated approaches to early detection. This study proposes a deep learning–based text classification framework using a long short-term memory (LSTM) network to identify mental health conditions from user generated textual data. A corpus of 103,488 labeled texts representing anxiety, stress, bipolar disorder, depression, personality disorder, suicidal ideation, and normal states was preprocessed through tokenization, padding, and word embedding. The proposed LSTM model achieved overall accuracy of 87% on test set, with strong class-wise performance reflected by precision, recall, and F1-scores, particularly for anxiety, personality disorder, and normal classes. Comparative error analysis using a confusion matrix revealed challenges in distinguishing depression from suicidal ideation, indicating semantic overlap between these conditions. The results demonstrate that LSTM-based models can effectively capture sequential linguistic patterns relevant to mental health classification. This framework shows potential as a decision-support tool for early screening and digital mental health applications, complementing clinical assessment rather than replacing it.
Genetic algorithm for generalized time-window assignment problem Kansou, Ali; Kanso, Bilal; Wehbe, Houssein; Bazzi, Haydar; Mcheik, Ali
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.pp1261-1274

Abstract

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.
Venture capital and risks in growth stages of artificial intelligence tech start-ups Aziz, Sara; Abd Rahim, Noorlizawati
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.pp1036-1049

Abstract

Venture capital (VC) investment is important for the growth and innovation of artificial intelligence (AI)-driven tech start-ups, which are often characterized by high uncertainty and rapid technological change. While existing literature has explored general risk factors in AI start-ups, however, limited understanding of how these risks vary across different stages of start up development. This study addresses this gap through a systematic literature review (SLR) of 29 studies published between 2019-2024, sourced from IEEE Xplore, Web of Science (WoS), Scopus, and ProQuest databases. Guiding investment lifecycle, risks management and ISO 31000 principles, the study identified key risks variations including market, operational, financial, technological, performance, regulatory and exit risks faced by AI tech start-ups during the seed and early, growth and maturity stages. Findings indicate that early-stage start-ups are more affected by funding, market entry, and feasibility risks, while at growth stage face issues with scaling and resource management, maturity stage with regulatory and exit related risks become more significant. A taxonomy matrix is developed to categorize these risks in a stage-specific and AI-relevant context. The review contributes to the literature by offering a structured understanding of how VC related risks evolve across start-ups stages and highlights the need for further empirical research to validate these findings and guide better investment decisions.
Enhancing digital asset ownership through decentralized non fungible token applications Kurnia, Yusuf; Rino, Rino; Edy, Edy; Junaedi, Junaedi; Hermawan, Aditiya; Kevin, Kevin
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.pp1972-1981

Abstract

The rapid expansion of the digital ecosystem has introduced pressing challenges surrounding identity, authenticity, trust, and transparency. The ease with which digital content can be duplicated often undermines creators, whose works are distributed without consent or fair compensation. Blockchain technology offers a transformative solution through its decentralized, transparent, and tamper-resistant structure. Among its innovations, non-fungible tokens (NFTs) provide a mechanism to verify the authenticity and ownership of unique digital assets. This study explores the transformative potential of NFTs in strengthening digital ownership and authenticity while identifying critical challenges such as market concentration, interoperability limitations, and security vulnerabilities within public NFT platforms. Employing the extreme programming (XP) methodology, this research proposes a secure framework for NFT creation outside public marketplaces to enhance the protection of smart contracts and user accounts. The findings demonstrate that this approach grants users’ greater control, minimizes exposure to platform-level risks, and promotes trust in decentralized asset management. Overall, this study underscores NFTs’ pivotal role in reshaping digital ownership models and highlights the need for continued innovation to ensure security, transparency, and equitable value distribution in the evolving digital economy.
Scaler enhanced deformable attention with graph neural network for video compression Kasinathaperumal, Revathi; Periapandi, Hosanna Princye
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.pp1473-1485

Abstract

Video compression is widely used to reduce bandwidth and storage requirements when storing and transmitting videos, most existing neural video compression approaches adopt the predictive residue-coding framework, which is suboptimal for removing redundancy across frames. Additionally, minimizing only the pixel-wise differences between the raw and decompressed frames is ineffective in improving the perceptual quality of the videos, blocking artifacts degrade the visual quality, especially near edges and texture areas. Hence, to solve these problems, this research proposes a scaler enhanced deformable attention graph neural network (SEDA-GNN) to utilized for reduce inter-frame redundancy by employing a deformable attention mechanism that efficiently captures motion and structural changes, thereby minimizing redundancy. Modelling complex temporal dynamics with graph neural networks (GNNs) captures dependencies between frames, thereby facilitating highly efficient video encoding, then constrained directional enhancement filter (CDEF) effectively reduces blocking artifacts while preserving sharp edges through directional and constrained filtering, thereby improving visual quality in compressed video. The SEDA-GNN approach achieved a bjontegaard delta bit rate (BD-BR) reduction of 2.372% on the joint collaborative team on video coding (JCT-VC) database and 3.230% of BD-BR on the ultra video group (UVG) dataset, demonstrating significant performance when compared to invertible neural networks (INNs).
Breast cancer detection using residual DenseNets in deep learning Gururajarao, Naganandini; R. Hulipalled, Vishwanath
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.pp1632-1645

Abstract

Breast cancer, the leading cause of cancer-related deaths among women globally, requires a prompt and precise diagnosis in order to increase survival rates via therapy. There is a possibility of bias and inconsistency in the results of traditional diagnostic procedures like mammography, ultrasound, and histological testing since they rely on the expertise of radiologists and pathologists. There are exciting new opportunities for breast cancer diagnostics to be enhanced by artificial intelligence (AI) and deep learning. The purpose of this research is to examine the feasibility of using convolutional neural networks (CNNs) and residual dense networks (ResDenseNets) used for breast cancer automated detection in medical images. Because of their superior capacity to learn hierarchical features from raw image data, CNNs are ideal for medical image interpretation. By including residual connections, which allow for the training of considerably deeper models, ResDenseNets—an extension of CNNs—mitigate the problem of vanishing gradient in deep networks. ResDenseNet and CNNs considerably enhance the accuracy of breast cancer diagnosis in comparison to conventional approaches, according to the findings. Notably, ResDenseNets outperform other types of networks because they are able to learn intricate and nuanced properties directly from the data.

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