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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 1,974 Documents
 189   190   191   192   193 
Prediction of new student admissions to higher education using support vector machines Neni Purwati; Windya Harieska Pramujati; A. Aviv Mahmudi; Mira Febriana Sesunan; Yahya Yahya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2484-2493

Abstract

Higher education institutions across various regions operate using systems that generate large amounts of data. This data is stored and utilized for strategic decision-making, providing significant business value to these institutions. Support vector machine (SVM) has become popular due to its strong generalization capability, high prediction accuracy, and faster training speed. SVM employs kernels as tuning parameters. This study aims to enhance the accuracy of student admissions prediction in higher education institutions using the SVM classification model. The SVM model was applied to a dataset comprising 5,936 records with four attributes and was evaluated using the use training set, 10-fold cross-validation, and percentage splits of 70%–30% and 80%–20%. Initially, the SVM-kernel model achieved high accuracy but failed to identify any true positive instances, indicating its inability to detect the minority “not accepted” class due to severe class imbalance. After applying class balancing techniques, the model’s performance improved significantly in terms of area under the curve (AUC), F-measure, and Matthews correlation coefficient (MCC), reflecting a more balanced classification between majority and minority classes. The SVM with Pearson VII function-based universal kernel (PUK) and classifier version 4.5 (C4.5) models achieved the best performance, indicating that class balancing effectively enhances both sensitivity and fairness in predictive classification.
Identification of areas of influence using a thematic modeling approach and belief function theory Fatima-Zahrae Sifi; Wafae Sabbar; Amal El Mzabi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2838-2848

Abstract

Social networks have become essential platforms for the dissemination of information and the exchange of ideas. They are transforming the way influencers interact and exert influence over their peers. On platforms such as Twitter, Facebook and Instagram, influence is expressed through social interactions such as retweets, likes, mentions, comments and shares. These activities play a key role in amplifying messages and shaping opinions. Studying the practices of influencers allows for a more precise identification of the domains in which their impact is particularly significant. However, this task is complex. It requires rigorous methods capable of integrating various forms of social engagement while managing the uncertainties associated with heterogeneous data. In this context, we propose a method to identify the domain of influence of a social media influencer. This approach combines thematic modeling Latent Dirichlet Allocation (LDA) with Belief Function Theory (BFT) to analyze social interactions and dominant topics of interest. By incorporating indicators such as retweets, likes and mentions, the method provides a robust framework for evaluating the influencer's impact across different domains. It thus offers precise tools for researchers, practitioners and decision-makers aiming to better understand these complex dynamics.
Ensuring effective cervical cancer diseases diagnosis system using ensemble machine learning model Oluwatobi Akinlade; Jafar Abdollahi; Misan Paul Etchie; Sunday Adeola Ajagbe; Oluwaseyi Omotayo Alabi; Bambo Ayo Adeyanju
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2410-2422

Abstract

Worldwide, cervical cancer is a major public health concern, and early detection is essential for successful management and treatment. Ensemble machine learning (ML) has emerged as a promising approach for improving the accuracy and reliability of cervical cancer diagnosis systems. This study evaluated the performance of various ensemble ML models for cervical cancer diagnosis using a large dataset of cervical cell images. The performance of different ensemble models was compared, including random forest (RF), gradient boosting (GB), and stacking, with conventional ML models, such as logistic regression and support vector machine (SVM). The bagging ensemble model developed reported training of 0.991667, 0.827586, 1.0, and 0.90566 for accuracy, precision, recall, and F-measure (F1-score), respectively. Furthermore, the interpretability of ensemble models was investigated using feature importance and partial dependence plots. The interpretability analysis revealed that ensemble models can provide valuable insights into the key features and factors that contribute to cervical cancer diagnosis. In conclusion, the findings suggest that ensemble ML is a promising approach for developing accurate and reliable cervical cancer diagnosis systems and improving the interpretability of these models.
Performance analysis of intelligent controllers for permanent magnet synchronous motor drive systems Kasim Mousa Al-Aubidy; Izziyyah M. Alsudi; Abdullah F. Al-Saoudi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2606-2617

Abstract

Permanent magnet synchronous motors (PMSMs) are utilized in robotics and automation applications due to their exceptional performance, compact dimensions, and minimal maintenance needs. To ensure dynamic operation and achieve outstanding performance, accurate rotor position detection and real-time control methods are required, independent of the motor's mathematical model. Several intelligent controllers based on soft-computing tools were designed and tested to evaluate the performance of the PMSM drive system. The performance of these intelligent controllers was compared to that of a conventional proportional-integral-derivative (PID) controller, which adjusts its parameters according to the mathematical model of the PMSM. The results indicate that the proposed intelligent controllers outperform the conventional PID controller in controlling the speed of the PMSM. The deep learning-based controller achieved the best results among all evaluated controllers, demonstrating rapid response, minimal overshoot (less than 0.35%), and improved capabilities for handling disturbances or changes in motor parameters.
Makespan and energy-aware workflow scheduler for heterogeneous cloud computing platform Rashmi Kambalapalli Anjaneya Reddy; Vikas Reddy Shivaram Reddy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2723-2735

Abstract

Scientific workflows, typically modelled as complex directed acyclic graphs (DAGs), are increasingly executed on heterogeneous cloud platforms to achieve high performance and scalability. However, as workflow sizes grow, energy consumption, and operational cost have become critical concerns, especially under global carbon-emission constraints. Although dynamic voltage and frequency scaling (DVFS) offers significant potential for energy savings, existing workflow scheduling methods fail to fully exploit heterogeneous processors that contain both high-performance and energy efficient cores, resulting in suboptimal makespan and energy utilization. To address this gap, the makespan and energy-aware workflow scheduler (MEAWS) is proposed as a multi-core DVFS-enabled scheduling framework designed to optimize both execution time and energy consumption in heterogeneous cloud environments. Extensive simulations using scientific workflows demonstrate that MEAWS reduces makespan by up to 88.75% and 70.4%, and lowers energy usage by 41.59% and 47.15% when compared with reliable and efficient workflow scheduling (REWS) and multi-objective workflow scheduling (MOWS). These improvements highlight the effectiveness of MEAWS in enhancing the sustainability and efficiency of scientific workflow execution.
Smart system to control lighting in a smart library Ouissale Elansari; Hicham Jamouli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2946-2955

Abstract

Smart light controllers revolutionize energy efficiency and convenience by controlling lights with precision. The main problem with such control structures is that the existing lighting systems are outdated and inefficient in places such as libraries, leading to unnecessary energy consumption. In this study, we propose the implementation of an intelligent light-emitting diode (LED) lighting control system that optimizes energy in libraries. The system utilizes passive infrared (PIR) sensors and a counter mechanism. When an aisle is occupied, the LEDs operate in highlight mode; however, they switch to medium-light mode when no presence is detected for a predetermined period. For this process, we propose a new control system with the random forest prediction algorithm for displacement patterns. We also designed a mathematical algorithm with three matrices derived from previous work on lighting. The algorithmic process is applied to ensure that the proposed system has enhanced maintainability, the simulation methodology generated scenarios based on occupancy data collected from real library environments, MATLAB was used to create dynamic simulations that reflected these user behaviors, with the power consumption of each LED calculated over an eight-hour operating period, the proposed system achieves potential savings between 23% to 39%.
Pre-driving fatigue screening from short-term heart rate variability with subject-independent validation Tia Haryanti; Eri Prasetyo Wibowo; Wahyu Kusuma Raharja; Rossi Septy Wahyuni; Imliyati Sari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2885-2895

Abstract

This study evaluates fatigue screening from 30-second electrocardiogram (ECG) recordings using short-term heart rate variability (HRV) features in a pre-driving context. The dataset comprises 99 participants (one session each) with fatigue labels derived from the Karolinska sleepiness scale (KSS), where the primary label (K1) defines non-fit as KSS ≥ 7. A subject-independent logistic-regression model was trained under a leave-one-subject-out (LOSO) scheme. Probabilities were calibrated using Platt scaling and evaluated through threshold-free metrics (receiver operating characteristic (ROC)-area under the curve (AUC), precision-recall (PR)-AUC) as well as calibration performance using the Brier score. The model achieved ROC-AUC =0.687 (95% confidence interval: 0.591–0.776), PR-AUC =0.621, and a Brier score of 0.200. At the operating threshold t = 0.255, the model achieved sensitivity of 1.000 with no false negatives, while specificity remained 0.091 (95% confidence interval: 0.030–0.140). Reliability analysis indicated reasonable calibration in the operational probability range. These findings support short-term HRV derived from ECG as a screening tool that prioritizes avoiding missed non-fit cases, paired with a triage scheme (fit/review/non-fit) to manage uncertainty near the decision threshold. Future work should incorporate ECG morphology and signal quality cues and aim to improve specificity without sacrificing sensitivity.
LogRegression: human action pattern recognition for yoga and kavayat for children Vanita Babanne; Pankaj M. Agarkar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2377-2384

Abstract

Poses of human pattern recognition through machine learning is an essential facet of several applications, including health, surveillance, and sports analysis. For children yoga and kavayat (mock drill) increases the physical as well as mental health. Through the analysis of motion data this work discriminates between varied actions with high precision, contribution valued insights for monitoring and analysis in real-time. Here, this system that influences technique called as machine learning specifically logistic regression, to precisely discriminate and classify physical action patterns (child pose estimation) of children. Analogous to the propagation of false information, identifying physical actions is essential for upholding integrity and efficiency across various domains. The novel system demonstrates a towering accuracy of 98.00%, highlighting its effectiveness in recognizing and classifying physical action patterns.
Handwriting-based personality classification on Indian samples using long-short term memory Pradeep Kumar Mishra; Gouri Sankar Mishra; Ali Imam Abidi; Tarun Maini; Amit Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2511-2520

Abstract

Traditional handwriting analysis methods have historically faced criticism for their lack of scientific basis, but more contemporary models based on layered artificial neural network (ANN) architecture have evidently been more successful. In the proposed model, a deep neural network (DNN) layered, long-short term memory (LSTM) model with contextual analysis has been proposed for handwriting-based personality classification. The model has been trained over a manually curated verbose dataset of ~6,000 Indian handwriting sample images, varying across genders, age groups, and regions. The classification is based on the five major personality traits. The proposed framework achieved an accuracy of 97.75%, which is over 10% better than the next best performing model on a comparably numerically bigger dataset; demonstrating the enhanced potential of context based neural networks on handwriting-based personality prediction when coupled with an appropriately varied and unbiased dataset.
Accurate detection of Alzheimer’s disease using machine learning model on magnetic resonance imaging data Rahman Md Mojnur; Md. Tanjil Sarker; Hezerul Abdul Karim; Fahmid Al Farid; Aziah Ali; Wan Noorshahida Mohd Isa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2357-2368

Abstract

The rapid identification and diagnosis of Alzheimer's disease (AD) is essential for initiating early intervention and effective treatment planning. Magnetic resonance imaging (MRI) provides valuable structural insights into the pathological alterations in the brain associated with AD. Early and accurate detection of AD is critical for initiating timely interventions. This study presents a classical machine learning (ML) approach for detecting AD using structured features extracted from MRI metadata, such as mini-mental state examination (MMSE) scores, brain volume metrics, and cognitive attributes. Unlike deep learning models that rely on raw imaging data, the interpretable framework offers reduced computational complexity and better alignment with real-world clinical constraints. Models such as random forest (RF) and extreme gradient boosting (XGBoost) achieved up to 85% accuracy, showing strong potential for deployment in resource-limited environments. The results demonstrate the potential of classical ML in supporting early AD diagnosis, particularly in low-resource clinical settings. Moreover, the proposed approach offers a computationally efficient and interpretable alternative to deep learning models, facilitating adoption in real-world healthcare environments.

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