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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 84 Documents
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Search results for , issue "Vol 14, No 4: August 2025" : 84 Documents clear
Optimized fault detection in bearings of rotating machines via batch normalization-integrated bidirectional gated recurrent unit networks kumar, Sujit; Kumar, Manish; Barde, Chetan; Ranjan, Prakash
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3334-3342

Abstract

Motor is commonly used in industrial applications. Although motors are frequently found to have bearing problems, this causes a serious safety risk to industrial production. Traditionally, fault diagnostics methods often required only signal processing techniques and are ineffective. To overcome this problem, deep learning (DL) has been recently developed rapidly and achieved remarkable results in fault diagnosis. The intelligent fault diagnosis and classification of rolling bearing faults based on ensemble empirical mode decomposition (EEMD) and batch normalization (BN), principal component analysis (PCA) based stacked bidirectional-gated recurrent unit (Bi-GRU) neural network, is proposed in this paper. BN is introduced to improve the fast convergence of gated recurrent unit (GRU). EEMD is applied to eliminate the noise interference from the vibrational signal, and then important features are selected using the correlation coefficient value. Next, PCA is utilized for dimensionality reduction to retain only the essential. Finally, the BN based stacked Bi-GRU model is developed to classify faults based on extracted features. The proposed model correctly classifies the different types of faults in real operating conditions and also compared with existing techniques.
Novel framework for downsizing the massive data in internet of things using artificial intelligence Firdose, Salma; Mishra, Shailendra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2613-2621

Abstract

The increasing demands of large-scale network system towards data acquisition and control from multiple sources has led to the proliferated adoption of internet of things (IoT) that is further witnessed with massive generation of voluminous data. Review of literature showcases the scope and problems associated with data compression approaches towards massive scale of heterogeneous data management in IoT. Therefore, the proposed study addresses this problem by introducing a novel computational framework that is capable of downsizing the data by harnessing the potential problem-solving characteristic of artificial intelligence (AI). The scheme is presented in form of triple-layered architecture considering layer with IoT devices, fog layer, and distributed cloud storage layer. The mechanism of downsizing is carried out using deep learning approach to predict the probability of data to be downsized. The quantified outcome of study shows significant data downsizing performance with higher predictive accuracy.
Optimizing citrus disease detection: a transferrable convolutional neural network model enhanced with the fruitfly optimization algorithm Ganadalu Lingaraju, Anoop; Mangala Shankaregowda, Asha; Sathiyamurthy, Babu Kumar; Budanoor Channegowda, Santhrupth; Jalapur, Shruti; Palahalli Chennakeshava, Chaitra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3201-3213

Abstract

Fungal, bacterial, and viral diseases significantly threaten citrus production and quality worldwide, prompting producers to explore technological solutions to mitigate the financial impact of these diseases. Image analysis techniques have emerged as powerful tools for detecting citrus diseases by differentiating between healthy and diseased specimens through the extraction of discriminative features from input images. This paper introduces a valuable dataset comprising 953 color images of orange leaves from the species Citrus sinensis (L.) Osbeck, which serves to train, evaluate, and compare various algorithms aimed at identifying abnormalities in citrus fruits. The development of automated detection systems is crucial for reducing economic losses in citrus production, with this research focusing on twelve specific diseases and nutrient deficiencies. We propose a novel approach to citrus plant disease detection utilizing a hyper-parameter tuned transferrable convolutional neural network (TCNN) model, referred to as the enhanced fruitfly optimization algorithm (EFOA)-TCNN model. This model optimizes the parameters of TCNN using the EFOA and enhances architectural design by incorporating three convolutional layers alongside an energy layer instead of a traditional pooling layer. Experimental results demonstrate that the proposed EFOA-TCNN model outperforms existing state-of-the-art methods, achieving a sensitivity of 0.975 and an accuracy of 0.995.
Urban incident detection based on hybrid convolutional neural networks and bidirectional long short-term memory Ayou, Meryem; Boumhidi, Jaouad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3153-3159

Abstract

Real-time incident detection is a major challenge in urban roads. This paper proposes an innovative hybrid method for incident detection, combining convolutional neural networks (CNN) and bidirectional-long short-term memory (Bi-LSTM). CNN extracts complex spatial features from raw data, while Bi-LSTMs are used for incident detection by capturing long-term temporal dependencies present in data. The proposed algorithm is evaluated using simulated data from the open-source software simulation of urban mobility (SUMO). This combination improves incident detection's accuracy and robustness by exploiting spatial and temporal information. Experimental results show that our hybrid approach outperforms the support vector machine (SVM), random forest (RF), and Bi-LSTM algorithms, with a substantial decrease in false positives and the speed of detecting urgent situations.
Semi-automatic voice comparison approach using spiking neural network for forensics Siddanakatte Gopalaiah, Kruthika; Chandrakant Nagavi, Trisiladevi; Mahesha, Parashivamurthy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2689-2700

Abstract

This paper explores the application of a semi-automatic technique using spiking neural network (SNN) approach for forensic voice comparison (FVC), addressing the limitations of traditional methods that are time-consuming and subjective. By integrating machine learning with human expertise, the SNN, which mimics the brain’s processing of temporal information, is applied to analyze Australian English voice data in .flac format. The model leverages synaptic connection strengths modified by spike timing, allowing for flexible voice feature representation. Performance metrics, including confusion matrices and receiver operating characteristic (ROC) analysis, indicate the model’s accuracy of 94.21%, highlighting the effectiveness of the SNN-based approach for FVC.
Comparing bidirectional encoder representations from transformers and sentence-BERT for automated resume screening Deshmukh, Asmita; Raut Dahake, Anjali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3404-3411

Abstract

In today’s digital age, organizations face the daunting challenge of efficiently screening an overwhelming number of resumes for job openings. This study investigates the potential of two state-of-the-art natural language processing models, bidirectional encoder representations from transformers (BERT) and sentence-BERT (S-BERT), to automate and optimize the resume screening process. The research addresses the need for accurate, efficient, and unbiased candidate evaluation by leveraging the power of these transformer-based language models. A comprehensive comparison between BERT and S-BERT is performed, evaluating their performance across multiple metrics, including accuracy, screening time, correlation with job descriptions, and ranking quality. The findings reveal that S-BERT outperforms BERT, achieving higher accuracy (90% vs. 86%), faster screening time (0.061 seconds vs. 1 second per resume), and stronger correlation with job descriptions (0.383855 vs. 0.1249). S-BERT though has a smaller vector size of 384 enables capturing richer semantic information compared to BERT’s vector size of 768, contributing to its superior performance. The study provides insights into the strengths and limitations of each model, offering valuable guidance for organizations seeking to streamline their talent acquisition processes and enhance candidate selection through automated systems.
Improving the transfer learning for batik besurek textile motif classification Marissa Utami; Utami, Marissa; Ermatita, Ermatita; Abdiansah, Abdiansah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3172-3181

Abstract

This proposed research discussion is a new combination model for classifying batik besurek fabric from the implementation transfer learning with mixed contrast enhancement, activation function, and optimizer method. The size of the batik besurek fabric motif image as an input image is 250×250 with three channels consisting of red, green, and blue totaling five classes, namely kaligrafi, rafflesia, burung kuau, relung paku and rembulan. All images in the dataset will be divided into train data (1540 images), validate data (380 images), and test data (480 images) that are taken directly from the batik store in Bengkulu. The division method used is stratified random sampling to take all the data, shuffles it, and divides the data sets for each class. Based on the experiment results, ResNet50 obtained the best performance compared to MobileNetV2, InceptionV3, and VGG16, with a training accuracy of 99.60%, a validation accuracy of 97.44%, and a testing accuracy of 98.12%. In the improvement experiment phase, the ResNet50 model with Adam optimizer, rectified linear unit (ReLU) activation function and contrast limited adaptive histogram equalization (CLAHE) as the contrast enhancement method obtained the highest test accuracy (98.75%), showing that CLAHE was very effective in improving performance on batik besurek data.
An optimal pheromone-based route discovery stage for 5G communication process in wireless sensor networks Siddaramu, Sinduja Mysore; Rekha, Kanathur Ramaswamy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2788-2796

Abstract

The rapid advancement of 5G communication underscores the need for heightened efficiency within wireless sensor networks (WSNs), where challenges such as data loss, inefficiency, and jitter are exacerbated by complex operations. This paper presents the optimal pheromone-based route discovery stage (OpRDS) algorithm, inspired by the natural foraging behaviors of ants, as a novel solution designed to optimize routing processes in the dynamic and demanding 5G environments. The study conducts a comparative analysis of OpRDS against traditional routing protocols, including the ad hoc on-demand distance vector (AODV), destination-sequenced distance-vector (DSDV), dynamic source routing (DSR), and zone routing protocol (ZRP), focusing on key performance metrics such as packet delivery ratio (PDR), latency, throughput, routing overhead (RO), energy consumption (EC), network lifespan, route discovery speed, and scalability. Our results reveal that OpRDS significantly outperforms the conventional protocols, evidencing a 2% increase in PDR, a 5.5% decrease in latency, a 6.7% rise in throughput, an 8.3% reduction in RO, an 11.1% decrease in EC (resulting in an 11% extension of network lifespan), a 10% improvement in route discovery speed, and a 6.7% enhancement in scalability. These findings highlight the algorithm's superior efficiency and adaptability in addressing the robust demands of 5G networks.
Efficient lung disease classification through luminescent feature selection using firefly algorithm Shanmugavelu, Anjugam; Joseph, Arul Leena Rose Peter
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3099-3108

Abstract

Over the past couple of decades, there has been a substantial increase in the prevalence of lung ailments, resulting in 3.5 million fatalities each year. This necessitates the adoption of a lung disease detection technology that is effective, trustworthy, and cost-effective. In this study, we propose an optimized convolutional neural network (CNN) model, used for multiclass categorization of lung ailments based on frontal chest X-rays. The classification includes four categories: COVID-19, viral pneumonia, lung opacity, and non-infectious normal group. We implemented the firefly algorithm to optimize the global efficiency of feature selection of the lung abnormality in the X-ray images of lung disease and COVID-19 to classify the input according to the target class. The proposed algorithm was tested for accuracy, precision, recall, and F1-score. The findings were validated using the transfer learning model VGG-16; the algorithm achieved a superior accuracy of 99.3% compared to that of other cutting-edge models such as Inceptionv3 and ResNet50.
Human sentiment analytics using multi model deep learning approach Kumar Muthevi, Anil; Venkatesh, Maganti; Adke, Pallavi Gaurav; Gadhave, Rajashree Tukaram; Vanguri, G L Narasamba; Srinivasulu, Thiruveedula
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3241-3252

Abstract

For assessing human beings, the measurement of willpower and human emotions plays an important role because human beings are emotional creatures. Emotional analysis, also known as sentiment analysis, is the process of using natural language processing (NLP) and machine learning to determine the emotions expressed in text, speech, or other forms of communication. However, critical emotional analysis is limited to human interactions only. Human emotional artificial intelligence or Human sentimental analytics, a sub domain of NLP seeks to improve this understanding. The Present study develops a model using multi model deep learning approach which is capable of efficiently understanding human emotions and their intentions, closely mirroring human cognition. By extending emotional analysis beyond the traditional limits, this model will collect broad ranging data to uncover clear and hidden emotional details. The primary objective of this paper is to build highly effective model which provides in-depth insights into human emotions, leading to logical conclusions depending on all available factors and reasons. The necessary input data for the current study will be collected from audio-visual media covering a vast range of audio and visual samples.

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