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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 123 Documents
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Search results for , issue "Vol 13, No 4: December 2024" : 123 Documents clear
Enhancing road image clarity with residual neural network dehazing model Martin, Aerun; Nazeri Kamaruddin, Mohd; Md Sani, Zamani; Abdul Ghani, Hadhrami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4147-4157

Abstract

Lane markers, or road markers, are the painted lines on a roadway that separate different lanes of traffic. Lane markers guide drivers and ensure orderly vehicle flow. They are essential for advanced driver assistance systems (ADAS), providing reference points for vehicle positioning on the road. These markers enable ADAS to give warnings, assistance, and automation features that enhance driver safety and convenience. However, unpredictable illumination, such as a foggy environment, can suppress marker visibility, impacting ADAS's performance. Deep learning-based methods are well-known for their superiority in handling various haze patterns. This paper presents a residual network (ResNet)-based deep learning model to improve road image clarity impacted by fog. The residual neural network dehaze model (RNN-D) utilises a joint loss function to produce haze-free images with improved lighting conditions and enhanced details. The model was trained, validated, and fine-tuned using hazy and corresponding non-hazy datasets to ensure that the model is quantitatively superior in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). RNN-D achieved an average PSNR of 27.98 and SSIM of 0.8 on multiple open sourced datasets. The proposed algorithm's superior performance and visually appealing results make it a powerful tool for real-world image dehazing applications.
Optimisation of semantic segmentation algorithm for autonomous driving using U-NET architecture Subhedar, Javed; R. Bachute, Mrinal; Kotecha, Ketan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3987-4002

Abstract

In autonomous driving systems, the semantic segmentation task involves scene partition into numerous expressive portions by classifying and labelling every image pixel for semantics. The algorithm used for semantic segmentation has a vital role in autonomous driving architecture. This paper's main contribution is optimising the semantic segmentation algorithm for autonomous driving by modifying the U-NET architecture. The optimisation techniques involve five different methods, which include; no batch normalisation network, with batch normalisation network, network with reduction in filters, average ensemble network, and weighted average ensemble network. The validation accuracy observed for the five methods were 90.28%, 91.68%, 89.80%, 92.04%, and 92.21% respectively. By reducing the filters in the network, the computation time reduces (Epoch time: 1 s 64 ms/step) as opposed to the typical (Epoch time: 4 s 260 ms/step), but the accuracy reduces. The optimisation techniques were evaluated for metrics like mean intersection over union (IoU), IoU for class, dice-metric, dice_coefficient_loss, validation loss, and accuracy. The dataset of 300 images used for this paper's study was generated using the open-source car learning to act (CARLA) simulator platform.
Bio inspired technique for controlling angle of attack of aircraft Bal, Subhakanta; Swain, Srinibash; Sarathi Khuntia, Partha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4206-4216

Abstract

This paper deals with the design of a proportional–integral (PI) controller for controlling the angle of attack of flight control system. For the first time teaching learning-based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed PI controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PI controller. The superiority of proposed approach is demonstrated by comparing the results with that of the conventional methods like genetic algorithm (GA) and particle swarm optimization (PSO). It is observed that TLBO optimized PI controller gives better dynamic performance in terms of settling time, overshoot, and undershoot as compared to GA and PSO based PI controllers. The various performance indices like mean square error (MSE), integral absolute error (IAE), and integral time absolute error (ITAE) are improved by using the TLBO soft computing techniques. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized PI controller gains are quite robust and need not be reset for wide variation in system parameters.
Guided imitation optimizer: a metaheuristic combining guided search and imitation search Daru Kusuma, Purba; Kallista, Meta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4217-4228

Abstract

This paper proposes a novel metaphor-free metaheuristic, namely the guided imitation optimizer (GIO). This metaheuristic combines the guided search and imitation-based search. There are five guided searches and three imitation based searches. Meanwhile, there are three references used in this metaheuristic: global finest, a randomly picked solution among the swarm, and a randomized solution within the search space. GIO is then evaluated by using 23 classic functions that consist of seven high dimension unimodal functions (HDUF), six high dimension multimodal functions (HDMF), and ten fixed dimension multimodal functions (FDMF). Through simulation, GIO is superior to golden search optimizer (GSO), grey wolf optimizer (GWO), puzzle optimization algorithm (POA), and coati optimization algorithm (COA) in handling most of these functions. GIO is the first finest in tackling seventeen functions and second finest in tackling six functions. Tight competition occurs between GIO and COA due to the performance of COA which becomes the second finest in handling most of these functions.
Accuracy based-stacked ensemble learning model for the prediction of coronary heart disease Bhutia, Santosini; Patra, Bichitrananda; Ray, Mitrabinda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4516-4525

Abstract

Coronary heart disease (CHD) is the primary cause of silent and noncommunicable deaths. Early detection is essential for slowing the progression of death and saving lives. Medical researchers use machine learning techniques to predict CHD. This article proposes an accuracy based-stacked ensemble learning (AB-SEL) model to predict CHD while minimizing computational time (CT). The dataset undergoes the logistic regression recursive feature elimination (LR-RFE) method to identify the important features. The three strong classifiers, logistic regression (LR), random forest (RF), and AdaBoost, are chosen to build ensemble machine-learning models, including techniques like bagging, majority voting, and stacking, for the Cleveland dataset accessible from Kaggle. Data scaling was done using the normal scalar method, and hyperparameter optimization was done using random search and grid search. Effectiveness is measured by accuracy, precision, recall, F1 score, and CT is validated through 5-fold cross-validation. The suggested approach achieved a 90.16% accuracy rate, required only 0.2 seconds of CT, and yielded an area under the curve (AUC) of 0.892.
WEKA-based machine learning for traffic congestion prediction in Amman City Arabiat, Areen; Hassan, Mohammad; Almomani, Omar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4422-4434

Abstract

Traffic congestion leads to wasted time, pollution, and increased fuel consumption. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman City, specifically at the 8th circle, using different ML classifiers. The 8th circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Datasets were collected from the greater Amman municipality hourly. The logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with the 8th circle. The waikato environment for knowledge analysis (WEKA) data mining tool is used to evaluate chosen classifiers by determining accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments have demonstrated that SVM is the best classifier to predict traffic congestion. The accuracy of SVM to predict traffic congestion at Westbound Street, Northbound Street, Eastbound Street, and Southbound Street was 99.4%, 99.7%, 99.6%, and 99.1%, respectively.
Image analysis for classifying coffee bean quality using a multi-feature and machine learning approach Septiarini, Anindita; Hamdani, Hamdani; Ery Burhandeny, Aji; Nurcahyono, Damar; Eka Priyatna, Surya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4241-4248

Abstract

Price and customer satisfaction depend on coffee bean quality. The coffee industry must analyze coffee bean quality. Global demand for robusta coffee is high. Coffee industry professionals mostly understand coffee bean quality. Thus, an image analysis using a computer vision-based approach for classifying robusta coffee bean quality is required. Image acquisition, region of interest (ROI) detection, pre-processing, segmentation, feature extraction, feature selection, and classification are covered in this study. A multi-feature derived based on color, shape, and texture features was employed in feature extraction, followed by feature selection using principal component analysis (PCA). Several machine-learning methods classified the coffee beans. The method performance was assessed using precision, recall, and accuracy. The selected features using the backpropagation neural network (BPNN) classifier outperformed others with 98.54% accuracy.
Driver inattention detection system using multi-task cascaded convolutional networks Soultana, Abdelfettah; Benabbou, Faouzia; Sael, Nawal; Bouhsissin, Soukaina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4249-4262

Abstract

Driver inattention has emerged as a critical concern impacting road safety, resulting in an alarming surge in accidents and fatalities. This research introduces a novel system for detecting inattention, structured across six levels: perception, facial feature extraction, tracking driver face, and driver secondary task using pre-trained deep learning models, inattention detection, risk estimation, and alert. The system is based on image processing captured from two strategically positioned cameras that simultaneously capture the driver’s activities while driving and their facial expressions. The second contribution concerns the driver facial features extraction using multi-task cascaded convolutional networks (MTCNN), and it is comparison with the histogram of gradient (HOG)-based frontal face detector, and haar feature based cascade classifier. The algorithms were compared based on their runtime efficiency, robustness in handling varying lighting conditions, and various head movements. The MTCNN achieves high performance, reaching accuracy levels ranging from 96.4% to 99.5% on two datasets including realistic driving scenarios: the DrivFace dataset and, the driver drowsiness dataset. The comparative analysis sheds light on the strengths and weaknesses of each algorithm, providing valuable insights for selecting the most suitable face detection algorithm to use in our system.
Pneumonia detection on x-ray image using improved depthwise separable convolutional neural networks Nur Alam, Islam; Zain Nabiilah, Ghinaa; Angela Sihotang, Erna Fransisca; Amirul Jabar, Bakti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4169-4177

Abstract

A single neural network model cannot capture intricate and diverse features due to its ability to learn only a finite set of patterns from the data. Additionally, training and utilising a single model can be computationally demanding. Experts propose incorporating multiple neural network models to address these constraints to extract complementary attributes. Previous research has highlighted challenges network models face, including difficulties in effectively capturing highly detailed spatial features, redundancy in network structure parameters, and restricted generalisation capabilities. This study introduces an innovative neural network architecture that combines the Xception module with the inverse residue structure to tackle these issues. Considering this, the paper presents a model for detecting pneumonia in X-ray images employing an improved depthwise separable convolutional network. This network architecture integrates the inverse residual structure from the MobileNetV2 model, using the rectified linear unit (ReLU) non-linear activation function throughout the entire network. The experimental results show an impressive recognition rate with a test accuracy of 97.24% on the chest x-ray dataset. This method can extract more profound and abstract image features while mitigating overfitting issues and enhancing the network's generalisation capacity.
Deep feature synthesis approach using selective graph attention for replay attack voice spoofing detection Palsapure, Pranita Niraj; Rajeswari, Rajeswari; Kempegowda, Sandeep Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4915-4926

Abstract

As voice-based authentication becomes increasingly integrated into security frameworks, establishing effective defenses against voice spoofing, particularly replay attacks, is more crucial than ever. This paper presents a novel comprehensive framework for replay attack detection that leverages the integration of advanced spectral-temporal feature extraction and graph-based feature processing mechanisms. The proposed system presents the design of a waveform encoder and a novel temporal residual unit for spectral and temporal feature extraction in synchronous. Further, an approach of selective attention graph followed by multi-scale feature synthesis is employed to retain precise and spoof indicative feature vectors at the classification layer. The proposed method addresses the significant challenge of distinguishing genuine speech from replayed recordings. The validation of the proposed model is done on the ASVSpoof2019 dataset to demonstrate the efficacy of the proposed approach. The proposed system outperforms existing methods, achieving a lower equal error rate (EER) of 0.015 and a reduced tandem detection cost function (t-DCF) of 0.503. The comparative outcome exhibits the robustness of the method in identifying replay attacks.

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