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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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Artificial intelligence for individuals with disabilities in higher education institutions: a systematic review Glory Roy, Finita; Johnson, Friggita
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.pp4454-4460

Abstract

With the growing integration of artificial intelligence (AI) in education, its potential to support students with disabilities in higher education remains significant but underexplored. This systematic review synthesizes existing literature on AI's effectiveness, barriers, and implications for inclusive education. Using the sample, phenomenon of interest, design, evaluation, and research type (SPIDER) framework, studies published between 2013 and 2024 were identified through a systematic search in databases such as PubMed, Scopus, Embase, Cochrane Library, and Google Scholar. Eighteen studies met the inclusion criteria, focusing on higher education settings and students with disabilities. The findings emphasize AI's role in enhancing accessibility, personalizing learning experiences, and fostering inclusiveness. However, persistent challenges include technological barriers, ethical concerns, and insufficient training. While AI holds transformative potential to support students with disabilities in higher education, addressing infrastructure gaps and ethical and training deficiencies is crucial for sustainable implementation and equitable learning environments.
Enhancing learning outcomes in smart education: a supervised machine learning predictive analytics model for course completion Bakhouyi, Abdellah; Dehbi, Amine; Amhaimar, Lahcen; Tazouti, Yassine; Nadir, Younes; Khalidi, Abderrahim
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.pp4711-4721

Abstract

Predictive analytics have become increasingly capable of delivering actionable and accessible feedback to enhance teacher performance to enhance student outcomes in higher education. This study introduces a supervised machine learning predictive model designed to forecast the duration required to complete a course in a video learning environment using a dataset of 8,665 statements from 490 students from National Higher School of Art and Design at Hassan II University in Casablanca over six academic years (2019-24). This paper analyzes decision trees (DT), random forest (RF), support vector machines (SVM), gradient boosting (GB), and linear regression (LR) techniques. The CMI-5 standard and JSON format are used to automatically transfer learning activity data from the learning management system (LMS) to the learning record store (LRS). The results indicate that DT, RF, and GB achieved 100 percent predictor accuracy.
Multi-phase feature selection for detection of epithelial ovarian cancer using ensemble machine learning techniques Subramanya, Suma Palani; Venkatapathiah, Suma Kuncha
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.pp4802-4813

Abstract

Epithelial ovarian carcinoma is one of the most prevalent causes of death. Timely ovarian cancer diagnosis is significant for bettering patient outcomes and rates of survival. For prognostic and diagnostic evaluation of malignancies, AI-based machine learning algorithms are used. This novel technique is undoubtedly an effective tool that may aid in selecting the best course of action. The collection of data comprising 150 patients contained an extensive selection of clinical characteristics and markers of tumors. The recursive feature elimination (RFE) and correlation coefficient feature selection techniques were assimilated to pick the features for the machine learning model, such as age, CA-125, tumor laterality, size, tumor type, grade of tumor, and International Federation of Gynecology and Obstetrics (FIGO) stage. The study’s findings indicate that the base model accuracy was around 96%, sensitivity 93%, and specificity 100%. Using ensemble classification, accuracy was around 96%, sensitivity 98%, and specificity 94% for the RFE technique. By obtaining a deeper understanding of their decision-making process, explainable artificial intelligence makes sophisticated machine learning methods easier to explain. Before beginning treatment, this research offers crucial data for the diagnosis and prognosis assessment of individuals with epithelial ovarian cancer (EOC).
Solving sparsity and scalability problems for book recommendations on e-commerce Ichsanudin, Muhammad; Handari, Bevina Desjwiandra; Wijanarko, Bambang Dwi; Hertono, Gatot Fatwanto
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.pp4865-4877

Abstract

This study proposed a hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and randomized singular value decomposition (RSVD) collaborative filtering (CF) method to overcome sparsity and scalability problems for book recommendations on e-commerce. CF is an information retrieval system that assumes a user has the same interest in an object as other users have in the past. When handling large volumes of data, sparsity problems can arise, where finding a similarity relation of user preferences results from a small assessment of an object by users. The scalability is the increased computation of an algorithm caused by increased users or objects, which makes recommendations take longer to form, therefore making them less accurate. HDBSCAN is a density-based clustering method that simplifies the hierarchical arrangement of the most significant clusters for extraction to group users in the same cluster. RSVD is a linear dimension reduction method that breaks a matrix into three sub matrices by reconstructing the size of that matrix without removing its dominant part, especially for cluster result matrices. The HDBSCAN RSVD-CF model reduced the root mean squared error (RMSE) by 21.83%, being 3793.73 seconds faster than the CF model. It also performed very well compared to both RSVD-CF and HDBSCAN-CF.
Impact of smoothing techniques for text classification: implementation in hidden Markov model Mathivanan, Norsyela Muhammad Noor; Mohd Janor, Roziah; Abd Razak, Shukor; Md. Ghani, Nor Azura
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.pp5183-5192

Abstract

A hidden Markov model (HMM) is widely used for sequence modeling in various text classification tasks. This study investigates the impact of different smoothing techniques, such as Laplace, absolute discounting, and Gibbs sampling on HMM performance across three distinct domains: e-commerce products, spam filtering, and occupational data mining. Through the comparative analysis, Laplace smoothing consistently outperforms other techniques in handling zero-probability issues, demonstrating superior performance in the e-commerce and SMS spam datasets. The HMM without any smoothing technique achieved the best results for job title classification. This divergence underscores the dataset-specific nature of smoothing requirements, where the simplicity of parameter estimation proves effective in contexts characterized by a limited and repetitive vocabulary. Hence, the findings suggest that tailored smoothing strategies are crucial for optimizing HMM performance in different textual analysis applications.
Evaluation of midwifery educated mobile applications for labor guidance and a roadmap for future developers Devi, Seeta; Rahane, Swapnil Vitthal; Podder, Lily; X., Sangeetha; Dimple, Kumari
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.pp5268-5278

Abstract

The objective of the study was to review the midwifery guided mobile apps for labor advice, assessing features, functions, and content relevance. In February to March 2024, midwifery labor-guided applications were reviewed in mobile platforms such as the Google Play Store and Apple iTunes Store. We used multimodal evaluation tools, such as the mobile app rating scale (MARS), specific statements, and IQVIA ratings, to assess the quality of these applications. The study evaluated midwifery-guided applications, resulting in an average objective quality score of 3.96±0.96 out of 5. 'Safe delivery' scored the highest rating of 4.94, followed by 'Pregnancy mentor' (4.89), 'Hypno-birthing' (4.61), 'Obstetrics 6th edition' (4.68), and 'MSD manual guide to obstetrics' (4.56). Functionality received the highest score (4.16±0.865), followed by information (3.99±0.97), engagement (3.88±1.07), and aesthetics (3.82±0.28) areas. Subjective quality score was 3.6±1.18 out of 5 for an overall MARS score of 3.76±1.02. Most applications received favorable reviews, indicating good quality, and it is recommended that future app developers design applications that include comprehensive information on labor management.
Unified BERT-LSTM framework enhances machine learning in fraud detection, financial sentiment, and biomedical classification Ndama, Oussama; Bensassi, Ismail; Ndama, Safae; En-Naimi, El Mokhtar
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.pp5081-5095

Abstract

The current paper proposes a hybrid framework based on the bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) networks for classification tasks in three diverse domains: credit card fraud detection (CCFD), financial news sentiment analysis (FNSA), and biomedical paper abstract classification (BPAC). The model leverages the strengths of BERT regarding the learning of contextual embeddings and those of LSTM in capturing sequential dependencies, thus setting the new state-of-the-art performance in each of the three domains. In the CCFD use case, the model was able to achieve an accuracy of 99.11%, considerably outperforming all the competing systems in fraud transaction detection. The BERT-LSTM model achieved a performance of 96.74% for FNSA, improving significantly in sentiment analysis. Finally, the use case of BPAC was robust, with 88.42% accuracy, which clearly classified biomedical abstract sections correctly. It is evident from the findings that this framework generalizes to a wide range of tasks and hence is an adaptable but strong tool in combating challenges of cross-domain classification.
Enhancing communication and interaction in the movie industry based SparkMLlib's recommendation system Chakouk, Said; Zitouni, Abdelkerim; Tchagafo, Nazif; Belaid, Ahiod
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.pp4661-4674

Abstract

In the ever-evolving landscape of streaming platforms, recommendation systems contribute significantly to enhancing the user experience. This article examines the significance of these systems in suggesting movies, analyzing their impact on user satisfaction and platform performance. Utilizing SparkMLlib, a powerful tool for large-scale data processing, we explore various recommendation techniques, including collaborative filtering and content-based filtering. We highlight the dimension of digital communication to further enhance the accuracy of recommendations and foster greater user engagement. Our study also addresses the challenges and future opportunities related to recommendation systems, emphasizing the need for transparency and ethical algorithms. This research highlights the potential for recommendation systems to revolutionize the digital entertainment landscape and shape the future of the movie industry.
A review of driver distraction detection while driving based on convolutional neural networks Alhamad, Ghady; Kurdy, Mohamad-Bassam
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.pp4415-4426

Abstract

Driver distraction represents a major cause of traffic accidents, posing a serious threat to human life. In this review, we present the latest research findings of driver distraction detection based on convolutional neural networks (CNNs). In general, the analysis of driver behavior while driving is represented by either detecting driver drowsiness or attention diversion from driving by other activities, all of which fall under the definition of driver distraction. Facial features are often the basis for detecting driver drowsiness. In most papers, it is typically done by eye blinking, yawning, and head movement. As for the driver attention diversion, it is through the position of the hand and face. It involves many activities, text messages, making phone calls, adjusting the radio, consuming beverages, reaching for objects behind the driver, applying makeup, interacting with passengers, and other similar distractions. However, suggesting new methodologies in driver distraction detection and choosing appropriate CNN-based techniques is a big challenge given the wide variety experiments and studies in this field. Therefore, previous papers should be revisited to produce new methods by taking advantage of the techniques used. As a result, this paper reviews research approaches and reveals the effectiveness of CNN in detecting driver distraction. Finally, the article lists techniques that can be used as benchmarks in this context.
Parametric optimization of microchannel heat exchanger using socio-inspired algorithms Gulia, Vikas; Nargundkar, Aniket
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.pp5303-5310

Abstract

Miniaturized products and systems have emerged as game-changing innovations with huge potential in the modern period with increasing emphasis on sustainable development and green energy. Automotive, astronomical, electronics, and medical research are just a few of the industries where micro electro mechanical systems (MEMS) have found use. In addition to that, microchannel heat exchangers (MCHX) have been created in response to the growing demand for effective cooling solutions for these small systems. Optimization of these MCHX is important for improving the overall system efficiency. In this work, two popular socio inspired evolutionary algorithms viz. teaching learning-based optimization (TLBO) and cohort intelligence (CI) are applied for optimizing three objectives such as power density, compactness factor, and heat transfer with pressure drop (HTPD) for air-water MCHX. The results obtained are significantly improved when compared with genetic algorithm (GA). Moreover, both the techniques are observed to be robust. This study investigates the use of socio-inspired artificial intelligence (AI) algorithms to support the design and optimization of heat exchangers, highlighting their potential to address complex engineering challenges more efficiently.

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