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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 1,722 Documents
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Integrating random forest and genetic algorithms for improved kidney disease prediction Raghavendr, Bommanahalli Venkatagiriyappa; Annigeri, Anandkumar Ramappa; Srikantamurthy, Jogipalya Shivananjappa; Sattigeri, Gururaj Raghavendrarao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2797-2804

Abstract

This work offers a novel method for predicting chronic kidney disease (CKD) by combining random forest (RF) classification with genetic algorithm (GA) to optimize important parameters. The dataset comprises 1,659 patients with 51 clinical parameters. The suggested method emphasizes the optimization of random state values, test size, and essential hyperparameters, such as the number of trees in the forest, the least number of samples needed at a leaf node, and the smallest number of samples necessary to split an internal node. The optimization process is conducted in two stages: the first stage optimizes the random state and test size, while the second stage focuses on hyperparameters. Through extensive simulations over 50 runs, the study demonstrates that the optimized model achieves an accuracy ranging from 0.9451 to 0.9738. The results indicate a maximum increase in accuracy of 2.09%, showcasing the effectiveness of the GA-RF integrated approach in enhancing model performance. This work provides valuable insights into the impact of parameter optimization on machine learning (ML) models, particularly in medical diagnostics, and offers a robust framework for developing highly accurate predictive models.
Transforming images into words: optical character recognition solutions for image text extraction Wadmare, Jyoti; Patil, Sunita; Kolte, Dakshita; Bhatia, Kapil; Desai, Palak; Wadmare, Ganesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3412-3420

Abstract

Optical character recognition (OCR) tool is a boon and greatest advancement in today’s emerging technology which has proven its remarkability in recent years by making it easier for humans to convert the textual information in images or physical documents into text data making it useful for analysis, automation processes and improvised productivity for different purposes. This paper presents the designing, development and implementation of a novel OCR tool aiming at text extraction and recognition tasks. The tool incorporates advanced techniques such as computer vision and natural language processing (NLP) which offer powerful performance for various document types. The performance of the tool is subject to metrics like analysis, accuracy, speed, and document format compatibility. The developed OCR tool provides an accuracy of 98.8% upon execution providing a character error rate of 2.4% and word error rate (WER) of 2.8%. OCR tool finds its applications in document digitization, personal identification, archival of valuable documents, processing of invoices, and other documents. OCR tool holds an immense amount of value for researchers, practitioners and many organizations which seek effective techniques for relevant and accurate text extraction and recognition tasks.
A deep learning-based framework for automatic detection of COVID-19 using chest X-ray and CT-scan images Kalli, Sivanagireddy; Kumar, Bukka Narendra; Jagadeesh, Saggurthi; Ravi Kumar, Kushagari Chandramouli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3192-3200

Abstract

COVID-19 has profoundly impacted global public health, underscoring the need for rapid detection methods. Radiography and radiologic imaging, especially chest X-rays, enable swift diagnosis of infected individuals. This study delves into leveraging machine learning to identify COVID-19 from X-ray images. By gathering a dataset of 9,000 chest X-rays and CT scans from public resources, meticulously vetted by board-licensed radiologists to confirm COVID-19 presence, the research sets a robust foundation. However, further validation is essential expanding datasets to encompass enough COVID-19 cases enhances convolutional neural network (CNN) accuracy. Among various machine learning techniques, deep learning excels in identifying distinct patterns on imaging characteristics discernible in chest radiographs of COVID-19 patients. Yet, extensive validation across diverse datasets and clinical trials is crucial to ensure the robustness and generalizability of these models. The conversation extends into complexities, including ethical considerations around patient privacy and integrating intelligent tech into clinical workflows. Collaborating closely with healthcare professionals ensures this technology complements the established diagnostic approach. Despite the potential to detect COVID-19 using chest X-ray imaging findings, thorough research and validation, alongside ethical deliberations, are vital before implementing it in the healthcare field. The results show that the proposed model achieved classification accuracy and F1 score of 96% and 98%, respectively, for the X-ray images.
Contract-based federated learning framework for intrusion detection system in internet of things networks Saputra, Yuris Mulya; Putri, Divi Galih Prasetyo; Putra, Jimmy Trio; Murti, Budi Bayu; Wahyono, Wahyono
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3324-3333

Abstract

A plethora of national vital infrastructures connected to internet of things (IoT) networks may trigger serious data security vulnerabilities. To address the issue, intrusion detection systems (IDS) were investigated where the behavior and traffic of IoT networks are monitored to determine whether malicious attacks or not occur through centralized learning on a cloud. Nonetheless, such a method requires IoT devices to transmit their local network traffic data to the cloud, thereby leading to data breaches. This paper proposes a federated learning (FL)-based IDS on IoT networks aiming at improving the intrusion detection accuracy without privacy leakage from the IoT devices. Specifically, an IoT service provider can first motivate IoT devices to participate in the FL process via a contract-based incentive mechanism according to their local data. Then, the FL process is executed to predict IoT network traffic types without sending IoT devices’ local data to the cloud. Here, each IoT device performs the learning process locally and only sends the trained model to the cloud for the model update. The proposed FL-based system achieves a higher utility (up to 44%) than that of a non-contract-based incentive mechanism and a higher prediction accuracy (up to 3%) than that of the local learning method using a real-world IoT network traffic dataset.
Application of self-organizing map for modeling the Aquilaria malaccensis oil using chemical compound Che Hassan, Mohammad Arif Fahmi; Mohd Yusoff, Zakiah; Ismail, Nurlaila; Taib, Mohd Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2889-2898

Abstract

Agarwood oil, known as ‘black gold’ or the ‘wood of God,’ is a globally prized essential oil derived naturally from the Aquilaria tree. Despite its significance, the current non-standardized grading system varies worldwide, relying on subjective assessments. This paper addresses the need for a consistent classification model by presenting an overview of Aquilaria malaccensis oil quality using the self-organizing map (SOM) algorithm. Derived from the Thymelaeaceae family, Aquilaria malaccensis is a primary source of agarwood trees in the Malay Archipelago. Agarwood oil extraction involves traditional methods like solvent extraction and hydro-distillation, yielding a complex mixture of chromone derivatives, oxygenated sesquiterpenes, and sesquiterpene hydrocarbons. This study categorizes agarwood oil into high and low grades based on chemical compounds, utilizing the SOM algorithm with inputs of three specific compounds: β-agarofuran, α-agarofuran, and 10-epi-φ-eudesmol. Findings demonstrate the efficacy of SOM-based quality grading in distinguishing agarwood oil grades, offering a significant contribution to the field. The non-standardized grading system's inefficiency and subjectivity underscore the necessity for a standardized model, making this research crucial for the agarwood industry's advancement.
A survey of missing data imputation techniques: statistical methods, machine learning models, and GAN-based approaches Sadegh, Rifaa; Mohameden, Ahmed; Salihi, Mohamed Lemine; Nanne, Mohamedade Farouk
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2876-2888

Abstract

Efficiently addressing missing data is critical in data analysis across diverse domains. This study evaluates traditional statistical, machine learning, and generative adversarial network (GAN)-based imputation methods, emphasizing their strengths, limitations, and applicability to different data types and missing data mechanisms (missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR)). GAN-based models, including generative adversarial imputation network (GAIN), view imputation generative adversarial network (VIGAN), and SolarGAN, are highlighted for their adaptability and effectiveness in handling complex datasets, such as images and time series. Despite challenges like computational demands, GANs outperform conventional methods in capturing non-linear dependencies. Future work includes optimizing GAN architectures for broader data types and exploring hybrid models to enhance imputation accuracy and scalability in real-world applications.
Federated deep learning intrusion detection system on software defined-network based internet of things Dhirar, Heba; Hamad, Ali H.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3109-3120

Abstract

The internet of things (IoT) and software-defined networks (SDN) play a significant role in enhancing efficiency and productivity. However, they encounter possible risks. Artificial intelligence (AI) has recently been employed in intrusion detection systems (IDSs), serving as an important instrument for improving security. Nevertheless, the necessity to store data on a centralized server poses a potential threat. Federated learning (FL) addresses this problem by training models locally. In this work, a network intrusion detection system (NIDS) is implemented on multi-controller SDN-based IoT networks. The interplanetary file system (IPFS) FL has been employed to share and train deep learning (DL) models. Several clients participated in the training process using custom generated dataset IoT-SDN by training the model locally and sharing the parameters in an encrypted format, improving the overall effectiveness, safety, and security of the network. The model has successfully identified several types of attacks, including distributed denial of service (DDoS), denial of service (DoS), botnet, brute force, exploitation, malware, probe, web-based, spoofing, recon, and achieving an accuracy of 99.89% and a loss of 0.005.
Unpacking the drivers of artificial intelligence regulation: driving forces and critical controls in artificial intelligence governance Atoum, Ibrahim; Altahat, Salahiddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2655-2666

Abstract

The burgeoning field of artificial intelligence (AI) necessitates a nuanced approach to governance that integrates technological advancement, ethical considerations, and regulatory oversight. As various AI governance frameworks emerge, a fragmented landscape hinders effective implementation. This article examines the driving forces behind AI regulation and the essential control mechanisms that underpin these frameworks. We analyze market-driven, state-driven, and rights-driven regulatory approaches, focusing on their underlying motivations. Furthermore, critical regulatory controls such as data governance, risk management, and human oversight are highlighted to demonstrate their roles in establishing effective governance structures. Additionally, the importance of international cooperation and stakeholder collaboration in addressing the challenges posed by rapid technological change is emphasized. By providing insights into the strengths, weaknesses, and potential synergies of different governance models, this study contributes to the development of equitable and effective AI regulatory frameworks that encourage innovation while safeguarding societal interests. Ultimately, the findings aim to inform policymakers, industry leaders, and civil society organizations in their efforts to foster a future where AI is utilized responsibly and equitably for the betterment of humanity.
Survey on 3D biometric traits for human identification Gangachannaiah, Divya; Shivaraj, Mamatha Aruvanalli; Nagaraj, Honganur Chandrasekharaiah; Paga, Prasanna Gururaj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3143-3152

Abstract

Individuals are verified and identified using Biometric technology based on their biological or behavioral traits. Biometric-based personal authentication systems are more reliable and user friendly, overruns the traditional personal authentication systems. The physiological biometric traits get abraded due to aging and massive work, while the behavioral biometric traits are having high variations due to external factors such as fatigue, and mood. Among the physiological biometric traits, Finger geometry patterns are widely deployed authentication system reason being its stability, user acceptability and uniqueness. Recent trends in Biometrics attempt to incorporate 3D domain traits, 3D reconstruction is done using 2D multiple images. 3D images are usually more robust and illumination invariant as compared to their 2D counterparts. 3D reconstruction algorithms are compared by finding mean square error (MSE).
Lightweight mutual authentication protocol for resource-constrained radio frequency identification tags with PRINCE cipher Naik, Mahendra Shridhar; Sreekantha, Desai Karanam; Sairam, Kanduri V S S S S; Nataraju, Chaitra Soppinahally
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3435-3443

Abstract

Radio frequency identification (RFID) is a key technology for the internet of things (IoT), with widespread applications in the commercial, healthcare, enterprise, and community sectors. However, privacy and security concerns remain with RFID systems. This manuscript presents a novel RFID-based mutual authentication protocol (MAP) using the PRINCE cipher to address these concerns. The proposed MAP leverages a PRINCE cipher architecture capable of both encryption and decryption based on a mode signal. It performs five encryption and two decryption processes during tag and reader mutual authentication, with updated seed values ensuring synchronization and secure data communication. The PRINCE cipher implementation utilizes less than 1% of slices, operates at 226 MHz with a latency of 3.5 clock cycles (CC), and has a throughput of 4.125 Gbps. The complete RFID-based MAP consumes 721 mW of power, occupies 2% of the chip area, and achieves a latency of 35.5 CC and a throughput of 262 Mbps. This represents a 25% reduction in latency, a 40% increase in throughput, and a 30% decrease in execution time compared to existing MAP approaches. The findings demonstrate the potential of the proposed MAP to enhance latency, throughput, and execution time, offering a promising solution for secure and efficient RFID authentication.

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