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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 2,143 Documents
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Sentiment-aware bidirectional long short-term memory model for depression detection from social media texts Cephas Paul Edward; Karthikeyan Harimoorthy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4249-4259

Abstract

The number of people living with depression is growing at an alarming rate. Depression impairs normal life, ranging from loss of performance in studies or work to being the cause for other health issues either due to lack of care or self-harm and in rare cases, even suicide. Early-detection is crucial for a better course of treatment. Younger generations usually tend to avoid seeking medical help due to the fear of being “judged” but on the other hand share their feelings on social media. There have been various studies in the past to detect depression from social media posts but detection accuracy is limited due to a number of factors such as informal language. Modeling emotional states from text continues to be a core challenge in human-centered artificial intelligence (AI). The sentiment-aware composite attention bidirectional long short-term memory (SCAB-Net) model aids in screening regular social media posts from the ones indicative of depression. The results show that the proposed model outperforms nine baseline models- support vector machine (SVM), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM),convolutional neural network (CNN), CNN LSTM, CNN BiLSTM, CNN with attention, BiLSTM with attention, CNN bidirectional gated recurrent unit (BiGRU) achieving an accuracy of 89.85%.
Hybrid neural network classification of knee osteoporosis using X-ray images and clinical features Nurfadjri Akbar Rizqi Basuki; Busono Soerowirdjo; Hustinawaty Hustinawaty; Iffatul Mardhiyah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4656-4666

Abstract

Osteoporosis is a degenerative disease marked by decreased bone density and increased fracture risk, particularly in the knee. Early detection is crucial, but conventional diagnostic methods are often subjective and require specialized skills. This study developed an artificial intelligence (AI) model for automatic osteoporosis classification using a hybrid method combining knee X-ray images and clinical data, integrating a convolutional neural network (CNN) for image features with an artificial neural network (ANN) for tabular data. The dataset comprised 239 patients with knee X-ray images and 27 clinical parameters. Pre-processing included handling missing values, encoding, and selecting the 20 best features via the analysis of variance (ANOVA) F-test, with T-score as the most influential parameter. Contrast-limited adaptive histogram equalization (CLAHE) improved image quality, while the synthetic minority oversampling technique (SMOTE) and image augmentation produced a balanced dataset of 462 samples. The hybrid model achieved 97.87% accuracy in classifying normal, osteopenia, and osteoporosis cases. Benchmarked against five alternative backbones (residual network (ResNet) 50V2, densely connected convolutional network (DenseNet) 121, mobile network (MobileNet) V2, vision transformer (ViT), and swin transformer) under the same framework, only the swin transformer matched this performance, while the others reached 74.47–82.98% accuracy, confirming the proposed backbone's contribution. These results show that combining imaging and clinical data improves diagnostic accuracy and supports AI-based early detection in orthopedics.
COVID-19 detection from medical images using hummingbird-optimized VGG16 framework Roshima Biju; Warish Patel; Kesavan Suresh Manic
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4806-4818

Abstract

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created a major diagnostic challenge worldwide, highlighting the need for reliable automated approaches to assist clinicians in detecting coronavirus disease 2019 (COVID-19) from medical images. This study introduces a unified detection framework designed to deliver robust and accurate classification performance across different imaging modalities. The proposed model was developed and evaluated on computed tomography (CT) and chest X-ray datasets. The workflow comprises five key stages: image acquisition and resizing, deep-feature extraction, handcrafted feature extraction, feature optimization, and binary classification. The proposed system achieved high diagnostic performance, yielding approximately 99.33% accuracy for CT images and about 99.40% accuracy for X-ray images. The results demonstrate the framework’s robustness and its potential as effective tool for computer-aided COVID-19 diagnosis.
Tripartite optimized framework for network based intrusion detection system Poonkody Iruthayaraj; Poornima Vivekanandhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4224-4233

Abstract

An unsecured network pays the way for various illegal access. Therefore, an efficient security system is needed to safeguard the data from attackers. Even though various network security methods such as authentication, authorization, and firewall have been used, the system still suffers from various types of security issues. An optimized soft voting classifier (OSOVOC) algorithm is proposed for network-based intrusion detection system (NIDS). It performs the hybridization of two algorithms namely: optimized decision tree (ODT) and optimized support vector machine (OSVM). All three algorithms are optimized using hyper parameter tuning. The final proposed model produces a more accurate result in an unbalanced dataset The primary focus of the proposed OSOVOC algorithm is to eradicate the false alarms, which significantly increases the true positive count and true negative count. The proposed OSOVOC algorithm produces negotiable false alarms, with performance reaching a F1-score of 100% during training phase and 99.87% during testing phase.
Hybrid model for driver drowsiness prediction using neuro-fuzzy and random forest Mouhcine Lakhloufi; El Mehdi Mellouli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4304-4314

Abstract

In this research, a new hybrid model designed was introduced to identify driver drowsiness by integrating an adaptive neuro-fuzzy inference system (ANFIS) with the random forest (RF) method. Driver drowsiness is one of the major risk factors for road accidents, and timely, accurate identification of it is vital for improving traffic safety. The main emphasis of our work is on the combination of ANFIS with RF. ANFIS uses fuzzy logic to represent intricate systems with uncertainties and non-linear relationships, on the other hand, RF offers powerful classification by its ensemble learning technique, performing well in high-dimensional feature spaces. This combination capitalizes on each model's strengths: ANFIS for its dynamic responding to uncertain data and RF for its high classification accuracy, robustness, and resistance to overfitting. The results from the different experiments confirmed that the hybrid ANFIS-RF outperformed its components considerably. It was able to return an enhancement in accuracy by 2.70% compared to ANFIS, and 0.16% compared to RF, while at the same time, dropping the mean squared error (MSE) by 33.40% compared to ANFIS and 2.85% compared to RF. Knowing that RF is already better than other traditional machine learning methods in classification, these results emphasize its major role in the effectiveness of the hybrid model.
An intelligent artificial intelligence framework for business insight generation from streaming data Benymol Jose; Sajimon Abraham; Nikitha Sajith
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4071-4080

Abstract

Big data analytics helps organizations leverage their data to uncover new opportunities, leading to smarter decisions, more efficient operations, and ultimately higher profits. Effective use of big data and creating important predictive models make it more straightforward for a business to realize what the consumer likes and what he or she does not. This will enable the business to propose better offers and administrations with their items when contrasted to its competitors. In this paper, the authors suggest the use of a big data analytics platform combined with natural language processing (NLP) techniques to do the intelligent processing of unstructured streaming data. A new method utilizing Apache Spark pipelining is proposed to achieve predictive analytics for unstructured data streams. With this predictive model, mining of the unstructured streaming data can be done and intelligent predictions are made possible in real time using artificial intelligence techniques.
Human identification and classification network based on object hierarchy Loai Alamro; Yuhanis Yusof; Nooraini Yusoff
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4412-4423

Abstract

In the real world, objects are categorized hierarchically. However, deep learning (DL)-based computer vision (CV) approaches typically learn to classify objects in a flat manner, which limits their ability to generalize to new objects and scenarios. To address this limitation, a new network called human identification and classification network (HICNet) is proposed for hierarchical-based object identification and classification. HICNet is a hierarchical object identification and classification network with feature extraction layers, an identification branch, and a classification branch. HICNet was evaluated on the labeled faces in the wild (LFW) and labeled faces in the wild-A (LFWA) datasets, where it achieved an identification accuracy of 99.27% and an average classification accuracy of 98.61%. These results demonstrate that HICNet is able to classify objects beyond the training sample(s) by tracing the hierarchy and correlation of the object. HICNet can be used in a variety of CV applications, such as visually impaired aids and robot guidance as it provides a comprehensive insight into the objects.
Optimizing cloud resource allocation using Gaussian convergence squirrel search algorithm Sidagouda Basagouda Patil; Mukund Anant Kulkarni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4175-4187

Abstract

Cloud computing has gained significant attention for efficiently executing requests according to users’ needs, providing quality services, and optimizing task execution time across virtual machines (VMs). However, resource allocation for various applications is difficult due to dynamic workload conditions and uncertainty in cloud computing. In this research, a Gaussian-based convergence factor (GCF) with the squirrel search algorithm (SSA) is used for allocating resources in a cloud environment for a small finance organization. The proposed GCF along with SSA balances workload and allocates many relevant resources to users’ applications while also ensuring deadline constraints. Tent chaotic map and GCF are incorporated in a traditional SSA to improve the search ability and convergence rate of SSA to allocate optimal resources in the cloud for a small finance organization. The selection of appropriate instance types that align with financial organization requirements includes choosing between on-demand, reserved, or spot instances. The GCF with SSA obtained less energy of 0.505 J, lower execution time of 0.472 s, less makespan of 0.723 s, and higher resource utilization of 51% for 100 tasks of 30 VMs, when compared to existing methods.
Energy efficient resource allocation using deep reinforcement learning with starling murmuration optimization in cloud Yogeetha Bangalore Rajanna; Anandaraj Shanthi Pichandi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4592-4601

Abstract

Cloud computing is one of the widely used technologies due to its advanced features like pay -per-use, scalability, flexibility, and many more. It initially aims at fulfilling users’ needs for accessing computing resources and purchasing cloud services as necessary. This is all done within the framework of on-demand resource sharing, which is facilitated by internet-based applications. Efficient resource allocation (RA) in the cloud includes challenges of high-dimensional state spaces and scalability, as the tasks keep increasing. To solve this problem, deep reinforcement learning (DRL) with Taylor-based optimization neighborhood starling murmuration optimization (TBONSMO) algorithm model is used for RA according to the tasks efficiently. The integration of DRL with deep neural networks enhances task scheduling by enabling adaptive and efficient RA. This approach improves the decision-making processes, optimizes energy usage as well as system performance in response to varying workloads and operational demands. The experimental results of the proposed TBONSMO method were calculated as follows: energy consumption of 30.58 kilo watt per hour and resource utilization of 99.54%, which is lesser when compared to the existing deep Q-network (DQN) approach.
An improved weighted metric genetic algorithm based feature selection approach for classification of phishing URLs Nandeesha Hallimysore Devaraj; Prasanna Bantiganahalli Thimappa; Prajna Shivarajappa; Parashiva Murthy Basavanapura Muddumadap
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4144-4158

Abstract

In recent years, websites have been collecting information from users for various purposes. However, users are often unaware that the collected information is being used only for its intended purpose. Phishing is a common social engineering attack that seeks to deceive users into revealing sensitive information, such as bank credentials or personal details, which can be exploited for malicious activities. Protecting this sensitive data from phishing attacks is critical in data security. Many existing approaches effectively classify phishing attacks but fail to distinguish based on the intent of the webpage. This study addresses this problem by proposing a keyword-specific web crawler focusing on extracting a novel feature similarity index based on web page content. Further, the method employs a genetic algorithm (GA) to select highly discriminating optimized features for the classification of phishing uniform resource locator (URLs). The weighted combination of different metrics is used to enhance the model accuracy. As per the performance analysis, the proposed model achieves 98.69% accuracy and 98.75% fitness value with a low false positive rate (FPR). The model interpretability is measured through Shapley additive explanations (SHAP). Additionally, the proposed method is compared with existing works, and experimental results indicate that the proposed model performance is superior over the existing solutions.

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