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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
 169   170   171   172   173 
A comprehensive impression on identifying plant diseases using machine learning and deep learning methodologies Motupalli, Ravikanth; Mesia Dhas, John T; Neerumalla, Swapna; Naga Ramesh, Janjhyam Venkata; Gouthami, Butti; Kumar Ande, Pavan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4694-4702

Abstract

Maintaining healthy plants is essential for long-term agricultural production because agriculture is the backbone of many economies. Agricultural productivity is greatly endangered by plant diseases, which result in huge economic losses. Identifying plant diseases using traditional approaches can be quite laborious, time-consuming, and knowledge-intensive. Automated, precise, and quick diagnosis of plant diseases has been made possible by recent developments in artificial intelligence, mainly in deep learning, and machine learning. This study gives a thorough analysis of how machine learning and deep learning are currently being used to detect plant diseases. Methodologies, datasets, evaluation measures, and the inherent difficulties of the area are all examined. In order to better understand these technologies in practical agricultural contexts, this review will try to shed light on their advantages and disadvantages.
Advancements in latent fingerprint recognition: a comprehensive review of techniques and applications Manchanda, Nandita; Singla, Sanjay; Rathinam, Gopal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4739-4748

Abstract

The identification of individuals has been in greater demand, whether it’s for criminal investigation, law enforcement, or the basic attendance marking system. Fingerprints are one of the most reliable and dependable methods for biometric identification systems; as such, they are crafted in the womb. Latent fingerprints refer to inadvertent impressions that are left behind at crime scenes and are of utmost importance in the field of forensic investigation and verification of the authenticity of an individual. However, because these impressions are unintentional, the quality of the prints uplifted is often poorer. To enhance the overall accuracy of fingerprint recognition, it is required to develop approaches that enhance the accuracy and reliability of existing techniques. Therefore, this paper provides a detailed analysis of the existing techniques for the reconstruction, enhancement, and matching of latent fingerprints.
Deep neural network solutions to Newell-Whitehead-Segel equations Nouna, Soumaya; Tammouch, Ilyas; Nouna, Assia; Mansouri, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5172-5182

Abstract

In this work, we use the deep neural network (DNN) approach called NeuroDiffEq, and the unified finite difference exponential approach for obtaining the approximated and exact solutions of Newell-Whitehead-Segel systems that are essential for the biology of mathematics. A unified approach was used to generate several solutions for solitary waves of those systems. The approximated solutions for selected studies are explored using the NeuroDiffEq approach, which is the artificial neural networks (ANN) approach and is based upon trial approximate solution (TAS). The comparison between the obtained approximated solutions and the analytical solutions indicates that the applied method has proved an efficient as well as a highly successful approach to solving various types of the Newell-Whitehead-Segel equations.
Predicting the severity of road traffic accidents Morocco: a supervised machine learning approach Touzani, Halima Drissi; Faquir, Sanaa; Yahyaouy, Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4461-4473

Abstract

Early prediction of road accidents fatality and injuries severity is one of the important subjects to road safety emphasizing the critical need to prevent serious consequences to reduce injuries and fatalities. This study uses real road accidents data set in Morocco. It represents the intersection between road safety and data science, aiming to employ machine learning techniques to provide valuable insights in accident’s severity prevention. The purpose of this paper is to study road accidents data in the country and combine results from statistical methods, spatial analysis, and machine learning models to determine which factors will mostly contribute to increase the accident’ severity in the country. A comparison of results obtained was also conducted in this paper using different metrics to evaluate the effectiveness of each method and determine the most important factors that contribute to increase the fatality or injuries severity in the specific context of accidents. The best prediction model was then injected into a proposed algorithm where more intelligent techniques are included to be implemented in a car engine to perform an early detection of severe accidents and therefore preventing crashes from happening.
A two-step intelligent framework for gene expression-based cancer diagnosis Haddou Bouazza, Sara; Haddou Bouazza, Jihad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4731-4738

Abstract

DNA microarray technology has advanced cancer diagnosis by enabling large-scale gene expression analysis, yet challenges remain in selecting relevant genes and achieving accurate classification. This study introduces two novel methods: the three-stage gene selection (3SGS) method and the statistics classifier (SC). By eliminating redundant, noisy, and less informative genes, the 3SGS method effectively lowers the dimensionality of gene expression data, while the SC classifier uses statistical measures of gene expression to classify samples with high accuracy and speed. Evaluated on leukemia, prostate cancer, and colon cancer datasets, the 3SGS method effectively identified minimal yet informative gene subsets, achieving 100% accuracy for leukemia, 99.3% for prostate cancer, and 97% for colon cancer. The SC classifier consistently outperformed traditional models in both accuracy and computational efficiency, completing predictions in under 2 seconds per dataset. Compared to conventional classifiers, it requires no parameter tuning and performs reliably even with small gene sets. While promising, future work should address multiclass classification and clinical validation to broaden the framework’s applicability. Together, these methods offer a precise and rapid cancer classification framework, supporting early diagnosis and personalized treatment strategies across diverse cancer types.
Humans’ psychological traits classification from their spending categories using artificial intelligence algorithms Gowda, Arpitha Chikkamagaluru Narasimhe; Ramachandra, Sunitha Madasi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4552-4564

Abstract

The analysis of human behavior data generated by digital technologies has gained increasing attention in recent years. Spending categories form a significant part of this digital footprint. In this study, we investigate the degree to which human expenditure records can be used to infer psychological traits from transaction data. A broad feature space was constructed, consisting of overall spending behavior, category-related spending behavior, and customer category profiles. These features were examined to identify their correlations with the Big Five personality traits. A dataset containing over 1,200 users’ transaction histories over three months was obtained from Kaggle. Personality trait labels were derived using a percentile-based classification method. Multiple AI algorithms: decision tree (DT), random forest (RF), logistic regression (LR), and support vector machine (SVM) were employed, along with a convolutional neural network (CNN) to classify personality traits. The CNN model, incorporating multi-dimensional convolutional layers and the full feature space, achieved a high accuracy of 99.03%. The outcomes of the experiment indicate the efficiency of combining behavioral features and AI models in psychological trait classification. The study also highlights ethical considerations, including privacy risks and misuse of inferred personality details.
Classification algorithm with artificial intelligence for the diagnostic process of obstructive sleep apnea Ventura-Tecco, Jehil; Fajardo-Avalos, Jesús; Cabanillas-Carbonell, Michael
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4520-4532

Abstract

Obstructive sleep apnea (OSA) is a disease that affects millions of people worldwide, and a large proportion of them remain undiagnosed due to the high cost of polysomnography (PSG) tests. For this reason, it is crucial to develop affordable diagnostic tools to facilitate early detection of this condition. This study aims to analyze how an artificial intelligence (AI) based classification algorithm impacts the diagnostic process of OSA in Lima, Peru. The algorithm was developed following the Kanban methodology, which guaranteed an efficient and transparent follow-up during the development cycle, which is key in the medical context where software quality and traceability are fundamental. A decision tree (DT) was used for diagnosis and classification, employing a training dataset provided by the National Sleep Research Resource (NSRR), from which six relevant attributes were selected for analysis. The research results indicated that, although the improvement in clinical diagnostic accuracy was minimal at 10.81%, positive results were obtained in other aspects: diagnostic time was significantly reduced by 28.17%, and the number of tests required decreased by 24.07%.
Classification of single origin Indonesian coffee beans using convolutional neural network Rifai, Achmad Pratama; Sari, Wangi Pandan; Rabbani, Haidar; Safitri, Tari Hardiani; Hajad, Makbul; Sutoyo, Edi; Nguyen, Huu-Tho
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5140-5156

Abstract

This research aims to develop a coffee bean type detection model using convolutional neural networks (CNN), leveraging a dataset of 14,525 images from 116 types of Indonesian coffee beans. Pre-processing steps including resizing, rescaling, and augmentation were applied to improve the dataset quality. The dataset was split into training, validation, and testing sets with proportions of 80%, 10%, and 10%, respectively. Two model development approaches were used: transfer learning with Inception V3 in two scenarios and a model built from scratch. The transfer learning Inception V3 model in scenario 1 achieved the best performance, with a test accuracy of 0.87 and optimal evaluation metrics across precision, recall, and F1-score. This model was fine-tuned using pretrained weights, allowing it to adapt effectively to the coffee bean dataset. The results highlight that transfer learning, especially with Inception V3, provides a robust method for classifying coffee beans, offering potential applications in the coffee industry for improving classification efficiency and accuracy. The study demonstrates how deep learning can enhance the objectivity and precision of coffee bean classification, contributing to greater consistency in product sorting and quality assessment.
Enhancing academic conferences with AI: defining the role of the human AI editor Galan-Cubillo, Esteban; Saez-Soro, Emilio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4484-4493

Abstract

Academic conferences serve as key platforms for knowledge exchange, yet they face challenges in managing large volumes of content efficiently while maintaining academic rigor. To address these challenges, this study introduces and evaluates the "AI editor": a novel human expert role who, using tools like ChatGPT, supervises, refines, and structures artificial intelligence (AI)-generated content in real time. Through a mixed-methods approach, we examine the role of AI in enhancing content creation and engagement. This approach included the experimental deployment of the AI editor in three sustainability-focused European academic conferences (in Spain and UK) and formative workshops with 127 university students from the same countries. While AI-assisted tools improve efficiency, concerns persist regarding traceability, reliability, and ethical oversight. Our findings indicate that AI by itself cannot guarantee scholarly integrity; continuous human oversight is indispensable. The AI editor ensures coherence, quality control, and compliance with academic standards, addressing a critical gap in AI adoption within research environments. This study contributes to the discourse on responsible AI use in academia by proposing a structured framework for its integration into conferences, balancing automation with human oversight. Moreover, it highlights the growing need for digital intelligence that enables researchers to interact ethically and effectively with AI and other digital technologies, fostering responsible and informed academic innovation.
Low-speed scalar control of induction motor by fuzzy logic Sevilla-Hidalgo, Alfonso Alejandro; Uscamaita-Quispetupa, Rossy; Herrera-Levano, Julio Cesar; Utrilla Mego, Limberg Walter; Coaquira-Castillo, Roger Jesus
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4623-4635

Abstract

Efforts have continually been directed toward optimizing processes in various fields, and the application in induction motors is no exception. Scalar control V/f offers a straightforward method to regulate the speed of a three-phase induction motor (TIM). However, it faces challenges at low speeds or proportionally at low frequencies, often failing to operate below 20% of its rated speed. This control typically pairs with a PI controller (PIC) for closed loop speed regulation, but its limited control range hinders performance at low speeds. Although intelligent methods have been developed to improve scalar V/f control, attention is often focused on high speeds, while control at low speeds is overlooked. This paper presents the simulation of a fuzzy controller (FC) with a Mamdani-type structure designed to achieve effective low-speed control of a TIM using the V/f scalar control technique. The results not only show improvements in overshoot and settling time but also reveal that the FC can control speeds as low as 6.06% of the rated speed, and it ensures a starting current below the designed motor current under load. Comparative analysis indicates that the FC outperforms the PIC in low-speed control, and it provides an optimal inrush current across different low speeds.

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