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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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Machine learning based COVID-19 study performance prediction Rahman, Md. Ataur; Rahman, Md. Sadekur; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1130-1139

Abstract

COVID-19 has impacted education worldwide. In this troublesome situation, it is hard enough for an institution to predict a student’s performance. Students’ performance prediction has always been a complex task and this pandemic situation has led this task to be more complex. The main focus of this work is to come up with a machine learning model based on a classical machine learning technique to predict the change in students’ performance due to COVID-19. Initially, some relevant features are selected, based on which the data are collected from students of some private universities in Bangladesh. After the entire data set is formed, we preprocessed the dataset to remove redundancy and noise. These preprocessed data are used for testing and training using the proposed model. The model is extensively evaluated in this way using three separate classical machine learning techniques, namely linear regression, k-nearest neighbors (k-NN), and decision tree. Finally, the results of the entire experiment follow, demonstrating the power of the machine learning model in such an application. It is observed that the proposed model with linear regression exhibits the best performance with an R2 error of 0.07% and an accuracy of 99.84%.
Performance assessment of time series forecasting models for simple network management protocol-based hypervisor data Afrianto, Yuggo; Munadi, Rendy; Setyorini, Setyorini; Widyanto, Toto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1150-1163

Abstract

Time series forecasting is vital for predicting trends based on historical data, enabling businesses to optimize decisions and operations. This paper evaluates forecasting models for predicting trends in simple network management protocol (SNMP)-based hypervisor data, essential for resource allocation in cloud data centers. Addressing non-stationary data and dynamic workloads, we use PyCaret to compare classical models like autoregressive integrated moving average (ARIMA) with advanced methods such as auto ARIMA. We assess 30 models on metrics including CPU utilization, memory usage, and disk reads, using synthetic and real-time datasets. Results show the naive forecaster model excels in CPU and disk read predictions, achieving low root mean squared errors (RMSE) of 0.71 and 869,403.35 for monthly and daily datasets. For memory usage predictions, gradient boosting with conditional deseasonalisation and detrending outperforms others, recording the lowest RMSE of 679,917.6 and mean absolute scaled error (MASE) of 4.46 on weekly datasets. Gradient boosting consistently improves accuracy across metrics and datasets, especially for complex patterns with seasonality and trends. These findings suggest integrating gradient boosting and naive forecaster models into cloud system architectures can enhance service quality and operational efficiency through improved predictive accuracy and resource management.
Recommender system for dengue prevention using machine learning Kajornkasirat, Siriwan; Hnusuwan, Benjawan; Puttinaovarat, Supattra; Puangsuwan, Kritsada; Kaewsuwan, Nawapon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1106-1115

Abstract

The study aimed to develop a recommender system for dengue prevention using environmental factors and mosquito larvae data. Data were collected from 100 households in Surat Thani, Thailand using mosquito larval survey in January 2020. Data mining techniques: frequent pattern growth (FP-Growth) and Apriori algorithms were used to find association rules and to compare accuracies for selecting a suitable model. The recommender system was designed as a web application. FP-Growth is more suitable for these data than Apriori algorithm. The factors associated with dengue infection, including community area, densely populated area, and agricultural area. Most areas where mosquito larvae are found are community areas and agricultural areas. Aedes larvae were found most in water containers with dark colors and without a lid. Aedes larvae were also found in small water jars, large water jars, cement tanks, and plastic tanks. The recommender system should be useful to dengue vector prevention and to health service communities, in planning and operational activities.
Deep learning-based classifier for geometric dimensioning and tolerancing symbols Bewoor, Laxmi; Bewoor, Anand; P. Hujare, Pravin; Rathod, Praveen; Yetekar, Vedant; Dollin, Shrish
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1345-1354

Abstract

This research investigates the recognition of geometric dimensioning and tolerancing (GD&T) symbols using a deep learning model for object detection. GD&T, playing a pivotal role in engineering and manufacturing, provides essential specifications for product design and production. Manual processes for GD&T are often time-consuming and error prone. The study demonstrates outstanding accuracy in automating GD&T symbol recognition in engineering applications using YOLOv8. A carefully curated dataset, encompassing a wide range of GD&T symbols, was employed for training and evaluating the model. The YOLOv8 architecture, renowned for its robust performance, was meticulously fine-tuned to cater to the specific requirements of GD&T symbol detection. This research not only addresses the challenges in manual GD&T processes but also showcases practical implications for improved quality control and streamlined engineering workflows. By automating GD&T symbol recognition, this study contributes to the efficiency and precision crucial in the engineering and manufacturing domains.
Security in smart cities using YOLOv8 to detect lethal weapons Rodriguez-Rosas, Ederson; Castillo-Turpo, Aron; Acuna-Condori, Kevin; Paiva-Peredo, Ernesto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp945-953

Abstract

The increase in the illegal use of lethal weapons at a global level has reachedworrying figures, resulting in an increase in assaults and armed robberies. Based on the above, closed circuit television (CCTV) surveillance systems emerge as an alternative solution. Therefore, the use of artificial intelligence is explored in order to detect the presence of lethal weapons in images accurately. In this study, a convolutional neural network model YOLOv8 is trained. A database including 4104 images with the presence of lethal weapons is generated. The Google Colab platform is used for the training phase, since it provides us with a free graphic processing unit (GPU), and the YOLOv8x and YOLOv8n models are used for comparison. The results indicate an advantage when using the YOLOv8 models, and when comparing them with similar models already proposed in the studied literature, we can conclude that our model stands out with an accuracy of 89.56% in the detection of lethal weapons. In conclusion, we were able to obtain a model capable of detecting lethal weapons in CCTV images, in addition to being able to be used in applications that require real-time detection. 
Multi-task deep learning for Vietnamese capitalization and punctuation recognition Nguyen, Phuong-Nhung; Thu-Hien, Nguyen; Nguyen, Truong-Thang; Thu-Nga, Nguyen Thi; Anh-Phuong, Nguyen Thi; Nguyen, Tuan-Linh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1605-1615

Abstract

Speech recognition is the process of converting the speech signal of a particular language into a sequence of corresponding content words in text format. The output text of automatic speech recognition (ASR) systems often lacks struc- ture, such as punctuation, capitalization of the first letter of a sentence, proper nouns, and names of locations. This absence of structure complicates compre- hension and restricts the utility of ASR-generated text in various applications, such as creating movie subtitles, generating transcripts for online meetings, and extracting customer information. Therefore, developing standardization solu- tions for the output text from ASR is necessary to improve the overall quality of ASR systems. In this article, we use the idea of multitask deep learning for the task of capitalization and punctuation recognition (CPR) for the output text of Vietnamese ASR, with the aim of the named entity recognition (NER) task as a supplement to help the CPR model perform better, and proposed to use text-to- speech (TTS) to create a dataset for CPR-NER multitask model training. The experiment results show that the multi-task deep learning model has improved CPR results by 6.2% of F1 score with ASR output and 7.1% on raw text.
Unveiling precision: Eye cancer detection redefined with particle swarm optimization and genetic algorithms Narwadkar, Sanved; Mehta, Pradnya Samit; Patil, Rutuja Rajendra; Kadam, Kalyani; Bidve, Vijaykumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1087-1095

Abstract

Eye cancer detection is rare. The study introduces a holistic swarm intelligence method for the timely identification and categorization of three significant eye disorders: glaucoma, diabetic retinopathy, and cataract. Glaucoma is distinguished by elevated pressure within the eye and harm to the optic nerve, potentially leading to permanent loss of vision. Diabetic patients experience diabetic retinopathy primarily due to the presence of high blood sugar levels. The early detection and classification of cataracts can be achieved by combining swarm intelligence algorithms such as particle swarm optimization (PSO) and genetic algorithms (GA). In the case of diabetic retinopathy diagnosis, swarm intelligence is employed to optimize the parameters of deep learning models, thereby enhancing the accuracy of lesion segmentation and classification. Cataract detection used to improve the evaluation of lens opacity and cloudiness, providing a robust diagnostic mechanism. The accuracy obtained with a PSO is 85.79%, F1 score 83.45%, and recall 82.43%. The accuracy obtained with a GA is 82.10%, F1 score 81.16%, and recall 81.51%. The comparison of GA, convolution neural network, and PSO algorithms proves that the accuracy to detect the eye cancer is achieved with PSO and GA algorithm.
A novel scalable deep ensemble learning framework for big data classification via MapReduce integration Varadharajan, Kesavan Mettur; Prem Kumar, Josephine; Ashwin, Nanda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1386-1400

Abstract

Big data classification involves the systematic sorting and analysis of extensive datasets that are aggregated from a variety of sources. These datasets may include but are not limited to, electronic records, digital imaging, genetic information sequences, transactional data, research outputs, and data streams from wearable technologies and connected devices. This paper introduces the scalable deep ensemble learning framework for big data classification (SDELF-BDC), a novel methodology tailored for the classification of large-scale data. At its core, SDELF-BDC leverages a Hadoop-based map-reduce framework for feature selection, significantly reducing feature-length and enhancing computational efficiency. The methodology is further augmented by a deep ensemble model that judiciously applies a variety of deep learning classifiers based on data characteristics, thereby ensuring optimal performance. Each classifier's output undergoes a rigorous optimization-based ensemble approach for refinement, utilizing a sophisticated algorithm. The result is a robust classification system that excels in predictive accuracy while maintaining scalability and responsiveness to the dynamic requirements of big data environments. Through a strategic combination of classifiers and an innovative reduction phase, SDELF-BDC emerges as a comprehensive solution for big data classification challenges, setting new benchmarks for predictive analytics in diverse and data-intensive domains.
DualVitOA: A dual vision transformer-based model for osteoarthritis grading using x-ray images Ruiyun, Qiu; Abdul Rahim, Siti Khatijah Nor; Jamil, Nursuriati; Hamzah, Raseeda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp925-932

Abstract

Knee osteoarthritis (OA) is a primary factor contributing to reduced activity and physical impairment in older individuals. Early identification and treatment of knee OA can assist patients in delaying the advancement of the condition. Currently, knee OA is detected early using X-ray images and assessed based on the Kellgren-Lawrence (KL) grading system. Doctors' assessments are subjective and can vary among different doctors. The automatic knee OA grading and diagnosis can assist doctors and help doctors reduce their workload. A new novel network called dual-vision transformer (ViT) OA is proposed to automatically diagnose knee OA. The network utilizes pre-processing technologies to process the data before doing classification operations using the Dual-ViT network. The suggested network outperformed neural networks like ResNet, DenseNet, visual geometry group (VGG), inception, and ViT in terms of accuracy and mean absolute error (MAE), and achieved an accuracy of 78.4 and MAE of 0.471, demonstrating its effectiveness.
Bridging biosciences and deep learning for revolutionary discoveries: a comprehensive review Tariq, Usman; Ahmed, Irfan; Khan, Muhammad Attique; Bashir, Ali Kashif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp867-883

Abstract

Deep learning (DL), a pivotal artificial intelligence (AI) innovation, has dramatically transformed biosciences, aligning with the surge in complex data volumes to foster notable progress across disciplines such as genomics, genetics, and drug discovery. DL's precision and efficiency outmatch conventional methods, propelling advancements in biomedical imaging and disease marker identification. Despite its success, DL's integration into broader bioscience areas encounters hurdles including data scarcity, interpretability challenges, computational demands, and the necessity for ethical and regulatory considerations. Overcoming these obstacles is vital for DL to achieve its transformative potential fully. This review explores into DL's expanding role in biosciences, critically examining areas ripe for DL application and highlighting underexplored opportunities. It provides an insightful analysis of the algorithms that form the backbone of DL in biosciences, offering a thorough understanding of their capabilities. Ultimately, this paper aims to equip biotechnologists and researchers with the knowledge to leverage DL effectively, thereby enhancing the analysis of complex bioscience data and contributing to the field's future advancements.

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