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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 1,808 Documents
 159   160   161   162   163 
Temporal context of lightweight network model for detecting boats approaching the tsunami early warning system Yogantara, Wayan Wira; Suprijanto, Suprijanto; Kusuma, Anak Agung Ngurah Ananda; Istianto, Yuki
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3542-3553

Abstract

The tsunami early warning system (TEWS) is a device that detects potential tsunamis. However, a boat that approaches TEWS is a source of communication disturbance. A convolutional neural network (CNN), as part of intelligent computer vision, is one solution for detecting boats and providing a warning to move away from the TEWS area. Water segmentation and refinement-temporal (WaSR-T), as the current advanced CNN network, exhibits impressive performance in detecting object obstacles in the marine domain, although it requires a powerful computational device. In the paper, we propose a modification of WaSR-T, replacing the most computationally intensive stages with a lightweight version called lightweight WaSR-T. On the proposed lightweight WaSR-T, the previous encoder of WaSR-T was replaced with MobileNetV3, and some feature layer maps were reduced as input to the decoder. For training and validating the lightweight WaSR-T, the image dataset representing the open sea and our extended dataset from Indonesia's ocean region were used. Based on the quantitative results and evaluation of the computational load, the sensitivity to detect a boat for WaSR-T and lightweight WaSR-T is 95.71% and 90.00%, respectively. The lightweight WaSR-T required less memory at 32.57%, resulting in a 0.0761% reduction in total processing time compared to the original WaSR-T. Therefore, our proposed lightweight WaSR-T is promising for use as the central part of an intelligent maritime computer vision system in TEWS.
A novel method for examining promoters using statistical analysis and artificial intelligence learning Sheet, Sinan Salim Mohammed; Al-Hatab, Marwa Mawfaq Mohamedsheet; Qasim, Maysaloon Abed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4006-4016

Abstract

Accurately classifying promoters has become a significant focus in bioinformatics research. Although numerous studies have attempted to address this challenge, the performance of existing methods still leaves room for improvement this study, statistical feature analysis has been applied to the features that have been developed in our previous work. This approach extracted additional informative features from basic sequence characteristics and then used them together with the original and newly engineered features. Utilizing statistical feature analysis enhanced key patterns, which lead to an improvement in the accuracy of the promoter classification. Results demonstrated that our proposed method outperforms other models that use only basic features. The value of the area under the curve (AUC) of 0.83958 achieved when using the combined feature set confirmed the effectiveness of our approach. Furthermore, the AUC value reached 1 when these optimized features were used with naive Bayes (NB) classifier, referring to the strength of incorporating statistical analysis into feature design.
Inverse-Mel scale spectrograms for high-frequency feature extraction and audio anomaly detection in industrial machines Tajuddin Shaikh, Kader Basha; P. Jawarkar, Naresh; Ahmed, Vasif; Ali Charniya, Nadir Nizar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3656-3666

Abstract

Unlike humans, the energies in industrial machine sounds (IMS) vary across a wide range of frequencies. Mel scales, which are developed for the perception of human audio, fail to capture the complete information present in IMS. To improve performance, we propose using an inverse-Mel scale, along with the concatenation and combination of Mel and inverse-Mel scale based spectrograms, as feature vectors for audio anomaly detection (AAD) in industrial machines. Adaptation in the Librosa Python package and the DCASE 2022 Challenge Task 2 baseline system is pursued for the construction of inverse-Mel scale spectrograms. Experiments are conducted using the malfunctioning industrial machine investigation and inspection for domain generalization (MIMII DG) datasets. Systems based on the inverse-Mel scale achieve a maximum improvement of up to 37% in the bearing machine and an average improvement of up to 9% in the area under the curve (AUC) score across all machines in the MIMII DG datasets. The proposed features also enhance DG, overcoming the effects of environmental and operational domain shifts caused by variations in recording setup, load, background noise, and operational patterns. Challenge official evaluator assessed the proposed system against the evaluation datasets, ranking it three positions higher than the baseline system.
Lung sound classification using YAMNet, neural network, and augmentation Arifin, Jaenal; Sardjono, Tri Arief; Kusuma, Hendra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4101-4112

Abstract

Globally, lung disease occupies a significant position as one of the main contributors to mortality rates. The characteristics of human respiratory sound signals can show a wide spectrum, ranging from normal patterns to indications of lung abnormalities. The proposed lung sound classification system is based on YAMNet as a pre-trained neural network model for medical audio recognition, which is then refined using artificial neural networks (ANN). This study presents the integration of multiple datasets and advanced pre-processing approaches. A total of 1,363 lung sound recordings from Kaggle, ICBHI, and Mendeley. This reflects the variety of clinical conditions, and differences in recording devices are combined. In order to increase the diversity of lung sound signal input, the pre-processing process is carried out through several stages, including adjusting the sampling frequency to 4 kHz, segmenting for 6 seconds, signal filtering with wavelet, min–max normalization, and data augmentation using window warping, jittering, cropping, and padding. A fold cross-validation scheme is employed to comprehensively evaluate the model's effectiveness. The evaluation results indicate that the model achieves an accuracy of 93.64%, a precision of 93.60%, a recall of 93.64%, and an F1-score of 93.52%, collectively reflecting outstanding classification performance. This work may incorporate deep learning technology into clinical practice, ultimately improving diagnosis accuracy and efficiency in the hospital setting.
Enhanced deepfake detection using an ensemble of convolutional neural networks Ralhen, Yeeshu; Sharma, Sharad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4043-4049

Abstract

Digital media integrity and authenticity have been seriously challenged with the rise of deepfakes. The challenge is to automatically detect this artificial intelligence (AI) generated manipulations. These manipulations or forgeries can cause harmful consequences such as spreading fake news in politics, scamming people online and invading privacy. Convolutional neural networks (CNN) models are found to be good at classification tasks, but the performance could not reach high accuracy, especially when they were tested on more challenging deepfake datasets. In this paper we present a deepfake detection system based on an ensemble of CNN architectures, ResNet50 and EfficientNet, capable of distinguishing between real and deepfake videos with high accuracy. For the experiment, we have chosen Celeb-DF version 2, as it has emerged to be one of the most challenging deepfake dataset. The ensemble model achieved an F1-score of 94.69% and an accuracy of 90.58%, outperforming the individual CNN models. This study shows that ensemble learning can increase the reliability and accuracy of deepfake detection systems on challenging datasets.
Laurent series intelligent multidimensional object optimization classification for crop disease detection Karunanithi, Anandhan; Singh, Ajay Shanker
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4050-4060

Abstract

Rice crop disease detection and its diagnosis methods are vitally important for the agriculture field to be sustainable. Traditional methods suffer from paddy yield, complex issues, and crop diseases, leading to inefficiencies in the agriculture domain. Our research provides space for a novel approach, combining the Laurent series with an intelligent multidimensional object optimization (LIMO) classification framework based on generative adversarial networks (GANs) to recognize various types of crop diseases in agricultural fields. Through our proposed research work, IoT nodes sense the values of the field crop, and gathered information is shared with processing units through base station communication. Multi-objective and cognitive learning routing (MOCLEAR) protocol supports choosing the optimal path for data transmission improvement. Then, for image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images. After receiving segment input images, perform feature extraction and classification using significant attributes. The proposed Laurent series with IMO is newly formulated by integrating the Laurent series with Intelligent IMO algorithms. Through extensive experimentation and analysis, the proposed LIMO-based GAN network provides effective and improved performance metrics with overall accuracy, sensitivity, and specificity values at 91.5%, 92.6%, and 92.41%, respectively. 
Accelerating solder joint classification using generative artificial intelligence for data augmentation Ong, Teng Yeow; Teoh, Chow Teoh; Tan, Koon Tatt
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4382-4389

Abstract

Despite advancements in computer vision, deploying deep learning algorithms for automated optical inspection (AOI) in printed circuit board (PCB) manufacturing remains challenging due to the need for large, diverse, and high-quality training datasets. AOI programs must be developed quickly, often as soon as the first PCB is assembled, to meet tight production timelines. However, deep learning models require extensive datasets of defect images, which are both scarce and time-consuming to collect. As a result, AOI software developers frequently resort to traditional rule-based methods. This study introduces a novel framework that leverages generative AI and discriminative AI to address dataset limitations. By applying a diffusion model to systematically add and remove Gaussian noise, the framework generates realistic defect images, expanding the available training data. This data augmentation accelerates the learning process of deep learning models, enhancing their robustness and generalizability. Experimental results demonstrate that this approach improves AOI system performance by producing balanced datasets across various defect classes. The framework shortens training times while maintaining high inspection accuracy, facilitating faster deployment of AOI systems in manufacturing. This advancement enhances quality control processes, contributing to more efficient, and reliable mass production of PCBs.
An algorithm for controlling the transmission of video streams in a flying ad hoc network M. Alghazali, Salah M.; Aljeazna, Wisam K. Mahdloom; Rasol, Murtadha N.; Polshchykov, Konstantin A.; V. Likhosherstov, Rodion
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4290-4298

Abstract

This article discussing the enhancement of video surveillance in various territories through the implementation of a flying ad hoc network (FANET). The primary objective of the surveillance is for search and rescue operations. To optimize the quality of FANET video broadcasting, a decision-making algorithm for video stream management is introduced. This algorithm evaluates the likelihood of achieving high-quality video transmission. Depending on the assessed probabilities, the algorithm recommends one of the following actions: initiating a new video stream transmission, reducing the average length of wireless channels, or discontinuing the transmission of low-information video streams. Computational experiments demonstrate a significant improvement in the accuracy of decision-making regarding the management of video stream transmission to FANET when utilizing the proposed algorithm.
Advanced risk assessment using machine learning and sentiment analysis on log data Turab, Nidal; Abushattal, Abdelrahman; Al-Nabulsi, Jamal; Owida, Hamza Abu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3897-3905

Abstract

Standard risk assessment approaches are sometimes time-consuming and subjective. In order to overcome these challenges an innovative method will be presented in this article by mixing sentiment analysis and machine learning (ML). The suggested technique improves the effectiveness, precision, and scope of risk insights when it comes to the detection of feelings in logs via the use of automated data collection. The research examines several different ML classifiers and makes use of a deep learning model that has been pre-trained to evaluate risks in logs that are multi-linguistic. This proves the adaptability and scalability of our technique when used in a multilanguage setting. This combination of sentiment analysis and ML are a significant advancement in comparison to traditional approaches since it enables real-time processing and delivers important insights into the management of organizational risks.
Hybridized deep learning model with novel recommender for predicting criticality state of patient using MIMIC-IV dataset Khope, Sarika; Kotambkar, Deepali; Adiraju, Rama Vasantha; Battalwar, Smita Suhas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3926-3933

Abstract

The contribution of machine learning towards prediction of critical state of patient is the prime focus of the current study. The review of current approaches of machine learning has been witnessed with various shortcomings. Hence, the proposed study adopts medical information mart for intensive care (MIMIC-IV) dataset in order to develop a novel analytical model that can predict the criticality state of patient in their next visit. The model has been designed by hybridizing convolution neural network (CNN) and long short-term memory (LSTM) which takes the discrete input of hospital and individual patient information in each visit. The concatenated feature is then subjected to a newly introduced recommender module which offers implicit feedback by assigning a ranking score. The final predictive outcome of study offers criticality rank. The study model is benchmarked with existing machine learning approaches to find 54% of increased accuracy and 70% of reduced processing time.

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