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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 81 Documents
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Search results for , issue "Vol 14, No 1: February 2025" : 81 Documents clear
Enhancement of YOLOv5 for automatic weed detection through backbone optimization Habib, Mohammed; Sekhra, Salma; Tannouche, Adil; Ounejjar, Youssef
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp658-666

Abstract

In the context of our research project, which involves developing a robotic system capable of eliminating weeds using deep learning technics, the selection of powerful object detection model is essential. Object detectors typically consist of three components: backbone, neck, and prediction head. In this study, we propose an enhancement to the you only look once version 5 (YOLOv5) network by using the most popular convolutional neural networks (CNN) networks (such as DarkNet and MobileNet) as backbones. The objective of this study is to identify the best backbone that can improve YOLOv5 's performance while preserving its other layers (neck and head). In terms of detecting and ultra-localizing pea crops. Additionally, we compared their results with those of the most commonly used object detectors. Our findings indicate that the fastest models among the networks studied were MobileNet, YOLO-tiny, and YOLOv5, with speeds ranging from 5 to 14 milliseconds per image. Among these models, MobileNetv1 demonstrated the highest accuracy, achieving average precision (AP) score of 89.3% for intersection over union (IoU) threshold of 0.5. However, the accuracy of this model decreased when we increased the threshold, suggesting that it does not provide perfect crop delineation. On the other hand, while YOLOv5 had a lower AP score than MobileNetv1 at an IoU threshold of 0.5, it exhibited greater stability when faced with variations in this threshold.
Explainable machine learning models applied to predicting customer churn for e-commerce Boukrouh, Ikhlass; Azmani, Abdellah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp286-297

Abstract

Precise identification of customer churn is crucial for e-commerce companies due to the high costs associated with acquiring new customers. In this sector, where revenues are affected by customer churn, the challenge is intensified by the diversity of product choices offered on various marketplaces. Customers can easily switch from one platform to another, emphasizing the need for accurate churn classification to anticipate revenue fluctuations in e-commerce. In this context, this study proposes seven machine learning classification models to predict customer churn, including decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), k-nearest neighbors (K-NN), and artificial neural network (ANN). The performances of the models were evaluated using confusion matrix, accuracy, precision, recall, and F1-score. The results indicated that the ANN model achieves the highest accuracy at 92.09%, closely followed by RF at 91.21%. In contrast, the NB model performed the least favorably with an accuracy of 75.04%. Two explainable artificial intelligence (XAI) methods, shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), were used to explain the models. SHAP provided global explanations for both ANN and RF models through Kernel SHAP and Tree SHAP. LIME, offering local explanations, was applied only to the ANN model which gave better accuracy.
Deep learning architectures for location and identification in storage systems Espitia Cubillos, Anny Astrid; Jimenez Moreno, Robinson; Rodríguez Carmona, Esperanza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp592-601

Abstract

This document exposes the application of two deep learning models based on ResNet-18 architectures, intended for the location and identification of products in storage areas. One model obeys a tree structure and the other a structure under an ouroboron cycle. The performance of both models is evaluated using the metrics of training time, processing time and level of learning precision, which allows recommendations to be made regarding which one should be used for order preparation purposes, based on multilevel feature extraction. The total training time of the first model is 34.65 minutes and the second 40.43 minutes. The analysis of results allowed the detection parameters to be adjusted, finally with the refined models, through confusion matrices, precision results greater than 90% and processing times are obtained, which for model 1 is 6.8565 seconds and for model 2 is 4.884 seconds. For practical purposes, training times are not relevant, as are the precision and processing times for selecting the most convenient model according to the end user's objectives.
Electroencephalogram denoising using discrete wavelet transform and adaptive noise cancellation based on information theory Abdolahniya, Hashem; Khazaei, Ali Akbar; Azarnoosh, Mahdi; Razavi, Seyed Ehsan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp769-779

Abstract

One of the most frequently used techniques for removing background noise from electroencephalogram (EEG) data is adaptive noise cancellation (ANC). Nonetheless, there exist two primary disadvantages associated with the adaptive noise reduction of EEG signals: the adaptive filter, which is supposed to be an approximation of contaminated noise, lacks the reference signal. The mean squared error (MSE) criterion is frequently employed to achieve this goal in adaptive filters. The MSE criterion, which only considers second-order errors, cannot be used since neither the EEG signal nor the EOG artifact are Gaussian. In this work, we employ an ANC system, deriving an estimate of EOG noise with a discrete wavelet transform (DWT) and input this signal into the reference of the ANC system. The entropy-based error metric is used to reduce the error signal instead of the MSE. Results from computer simulations demonstrate that the suggested system outperforms competing methods with respect to root-mean-square-error, signal-to-noise ratio, and coherence measurements.
New method for assessing suicide ideation based on an attention mechanism and spiking neural network Francis, Corrine; Al-Hababi, Abdulrazak Yahya Saleh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp350-357

Abstract

The COVID-19 pandemic has had a substantial effect on global mental health, leading to increased depression and suicide ideation (SI), particularly among young adults. This study introduces a novel method for enhancing SI assessment in young adults with depression, utilizing machine learning (ML) techniques applied to structural magnetic resonance imaging (SMRI) data. SMRI data from 20 individuals with depression and 60 healthy controls were analyzed. A hybrid ML algorithm, integrating self-attention mechanism and evolving spiking neural networks, successfully classified depression with 94% accuracy, 100% sensitivity, 92% specificity, and an area under the curve of 0.96. These results offer potential for enhancing mental health intervention and support in the context of the ongoing and post-pandemic period influenced by COVID-19.
Levenberg-Marquardt-optimized neural network for rainfall forecasting Rudrappa, Gujanatti; Vijapur, Nataraj; Hosamane, Sateesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp182-192

Abstract

Rainfall is a crucial meteorological indicator with applications in agriculture, aviation, and military. Forecasting is essential due to unpredictable environmental changes. Current methods use complex statistical models, which are timeconsuming. The present study is targeted for forecasting rainfall with the help of meteorological parameters, viz., temperature, humidity, wind speed, wind direction, and rain, using a specialized artificial intelligence (AI) method and real-time data captured over the study area. The weather station installed at KLE Dr. M. S. Sheshgiri College of Engineering and Technology in Karnataka, India, collects meteorological data. The models used were principal component regression (PCR) and Levenberg-Marquardt -optimized neural network (LMAONN). The Levenberg-Marquardt (LMA) backpropagation (BP) algorithm performed better than other BP algorithms. The coefficient of determination (R2) observed for the PCR and LMAONN models were 0.57 and 0.87, respectively. The LMAONN model provided a better fit for rainfall forecasting than the PCR model, with an index of agreement (IoA) of 0.96, indicating good forecasting.
Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning Mani, Sathishkumar; Kishoreraja, Parasuram Chandrasekaran; Joseph, Christeena; Manoharan, Reji; Theerthagiri, Prasannavenkatesan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp492-499

Abstract

The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the data is stored in centralized server in order to communicate between the systems. On the other hand, federated learning is a distributed learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The proposed research uses a hybrid IDS for wireless sensor networks using federating learning. The detection takes place in real-time through detailed analysis of attacks at different levels in a decentralized manner. Hybrid IDS are designed for node level, cluster level and the base station where federated learning acts as a client and aggregated server.
Detection and avoidance of black-hole attack in mobile adhoc network using bee-ad-hoc on-demand distance vector Pala, Srikanth; Maddula, Prasad; Pokkuluri, Kiran Sree; Pattem, Sunil; Kurada, Ramachandra Rao; Yadavalli, Ramu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp822-832

Abstract

Mobile adhoc networks (MANETs) are self-configuring networks with a dynamic infrastructure suit for real world applications. Due to the exponential increase in the network devices an efficient routing algorithm for dynamic network adhering the security issues is a critical challenge needs to be addressed. This article attempts to address this issue with the implemention of ad-hoc on-demand distance vector (AODV) routing approach, which is the best of its kind in the dynamic network design of MANETs. The primary goal is to address security attack weaknesses through the implementation of dynamic topologies and reactive routing. To this end, a bio-inspired swarm intelligence algorithm called Bees algorithm is used to emulate the AODV technique. In order to provide a lightweight solution that integrates the Bee algorithm and AODV routing, this study presents a unique algorithm called Bee-AODC. The proposed Bee-AODC algorithm possess the both the AODV's dynamic topology construction capabilities and the Bee algorithm's foraging strategy which effectively address security weaknesses by creating a dynamic network topology for ad hoc routing. By using the suggested Bee-AODC algorithm instead of the traditional AODV routing method, throughput is increased by 12.87% while packet loss, latency, and energy consumption are reduced by 20%, 40%, and 18%, respectively.
Predicting enhanced diagnostic models: deep learning for multi-label retinal disease classification Sundararajan, Sridhevi; Ramachandran, Harikrishnan; Gupta, Harshita; Patil, Yashraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp54-61

Abstract

In this study, we assess three convolutional neural network (CNN) architectures—VGG16, ResNet50, and InceptionV3 for multi classification of fundus images in the retinal fundus multi-disease image dataset (RFMID2), comprising of 860 images. Focusing on diabetic retinopathy, exudation, and hemorrhagic retinopathy, we preprocessed the dataset for uniformity and balance. Using transfer learning, the models were adapted for feature extraction and fine-tuned to our multi-label classification task. Their performance was measured by subset accuracy, precision, recall, F1-score, hamming loss, and Jaccard score. VGG16 emerged as the top performer, with the highest subset accuracy (84.81%) and macro precision (95.83%), indicating its superior class distinction capabilities. ResNet50 showed commendable accuracy (79.75%) and precision (86.70%), whereas InceptionV3 lagged with lower accuracy (66.67%) and precision (81.21%). These findings suggest VGG16’s depth offers advantages in multi-label classification, highlighting InceptionV3’s limitations in complex scenarios. This analysis helps optimize CNN architecture selection for specific tasks, suggesting future exploration of dataset variability, ensemble methods, and hybrid models for improved performance.
Implementation and evaluation of Heskes self organizing map counter propagation network for face recognition Olagunju, Kazeem Michael; Oke, Alice Oluwafunke; Falohun, Adeleye Samuel; Adebiyi, Marion O.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp204-212

Abstract

Face recognition has attracted a lot of interest in the fields of computer vision and pattern recognition given its extensive applications in security, surveillance, and human-computer interaction. Many linear and non-linear classifiers have been introduced to bring about effectiveness in face recognition, however, the problem of occlusion, light conditions and changes in face persist. The Heskes-self-organizing map (SOM) counter propagation network (CPN) model leverages the competitive learning and self-organizing features of SOM CPN with Heskes layer to improve the effectiveness and accuracy of face recognition systems. Heskes-SOM CPN was implemented and evaluated on MATLAB R2016a using 600 images captured with the aim of digital camera. The implemented model was trained with 360 face images and tested with 240 face images using accuracy, sensitivity, specificity, and false positive rate as performance metrics at four distinct threshold values of 0.23, 0.35, 0.50, and 0.75. The major objective of the research was achieved by investigating with 50×50 and 200×200 face dimensions. Empirical results and statistical evidence established that Heskes-SOM CPN has high accuracy of approximately 97.92%, high specificity of 98.33%, high sensitivity of 99.44%, and a very low filter performance rating (FPR) of 1.67%. Therefore, Heskes-SOM CPN is presented as a novel CPN model for face recognition.

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