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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 120 Documents
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Search results for , issue "Vol 13, No 1: March 2024" : 120 Documents clear
Explainable ensemble technique for enhancing credit risk prediction Nooji, Pavitha; Sugave, Shounak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp917-924

Abstract

Credit risk prediction is a critical task in financial institutions that can impact lending decisions and financial stability. While machine learning (ML) models have shown promise in accurately predicting credit risk, the complexity of these models often makes them difficult to interpret and explain. The paper proposes the explainable ensemble method to improve credit risk prediction while maintaining interpretability. In this study, an ensemble model is built by combining multiple base models that uses different ML algorithms. In addition, the model interpretation techniques to identify the most important features and visualize the model's decision-making process. Experimental results demonstrate that the proposed explainable ensemble model outperforms individual base models and achieves high accuracy with low loss. Additionally, the proposed model provides insights into the factors that contribute to credit risk, which can help financial institutions make more informed lending decisions. Overall, the study highlights the potential of explainable ensemble methods in enhancing credit risk prediction and promoting transparency and trust in financial decision-making.
Machine learning-based stress classification system using wearable sensor devices Chandra, Varun; Sethia, Divyashikha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp337-347

Abstract

University students often become victims of high-stress levels due to the highly competitive work environment. Unmonitored stress levels in students can inflict severe physiological health problems. This work aims to build a stress classification framework using wearable sensor devices to predict mental stress levels for undergraduate engineering students. It comprises a study to collect a data set of 23 university students using wearable devices for four physiological signals, i.e., electroencephalogram (EEG), electrodermal activity (EDA), skin temperature (SKT), and heart rate (HR), when the students perform the montreal imaging stress task (MIST) for the mental workload. The machine learning models proposed in this work help classify stress into three levels: rest, moderate, and high. The models achieve a classification accuracy of 99.98% using the EEG signals’ time-frequency domain features and an accuracy of 99.51% using the EDA, HR, and SKT signals. The proposed models achieve better scores than all the previous studies on stress classification, using EEG signals and EDA, HR, and SKT signals. This study is novel since it also demonstrates the applicability and proficiency of wearable sensor devices in developing accurate stress classification models to help build real-time stress monitoring systems.
Deep neural network for lateral control of self-driving cars in urban environment El Farnane, Abdelhafid; Youssefi, My Abdelkader; Mouhsen, Ahmed; El Ihyaoui, Abdelilah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1014-1021

Abstract

The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control. 
Choosing allowability boundaries for describing objects in subject areas Lolaev, Musulmon; Madrakhimov, Shavkat; Makharov, Kodirbek; Saidov, Doniyor
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp329-336

Abstract

Anomaly detection is one of the most promising problems for study and can be used as independent units and preprocessing tools before solving any fundamental data mining problems. This article proposes a method for detecting specific errors with the involvement of experts from subject areas to fill knowledge. The proposed method about outliers hypothesizes that they locate closer to logical boundaries of intervals derived from pair features, and the interval ranges vary in different domains. We construct intervals leveraging pair feature values. While forming knowledge in a specific field, a domain specialist checks the logical allowability of objects based on the range of the intervals. If the objects are logical outliers, the specialist ignores or corrects them. We offer the general algorithm for the formation of the database based on the proposed method in the form of a pseudo-code, and we provide comparison results with existing methods.
Handwritten digit recognition using quantum convolution neural network Daniel, Ravuri; Prasad, Bode; Pasam, Prudhvi Kiran; Sudarsa, Dorababu; Sudhakar, Ambarapu; Rajanna, Bodapati Venkata
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp533-541

Abstract

The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
Investigating optimal features in log files for anomaly detection using optimization approach Ranga, Shivaprakash; Mohankumar, Nageswara Guptha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp287-295

Abstract

Logs have been frequently utilised in different software system administration activities. The number of logs has risen dramatically due to the vast scope and complexity of current software systems. A lot of research has been done on log-based anomaly identification using machine learning approach. In this paper, we proposed an optimization approach to select the optimal features from the logs. This will provide the higher classification accuracy on reduced log files. In order to predict the anomalies three phases are used: i) log representation ii) feature selection and iii) Performance evaluation. The efficacy of the proposed model is evaluated using benchmark datasets such as BlueGene/L (BGL), Thunderbird, spirit and hadoop distributed file system (HDFS) in terms of accuracy, converging ability, train and test accuracy, receiver operating characteristic (ROC) measures, precision, recall and F1-score. The results shows that the feature selection on log files outperforms in terms all the evaluation measures.
Embedded artificial intelligence system using deep learning and raspberrypi for the detection and classification of melanoma Dahdouh, Yousra; Anouar, Abdelhakim Boudhir; Ahmed, Mohamed Ben
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1104-1111

Abstract

Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Using deep neural networks in classifying electromyography signals for hand gestures Al-Khazzar, Ahmed M.; Altaweel, Zainab; Hussain, Jabbar S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp217-227

Abstract

Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model. 
Toward accurate Amazigh part-of-speech tagging Bani, Rkia; Amri, Samir; Zenkouar, Lahbib; Guennoun, Zouhair
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp572-580

Abstract

Part-of-speech (POS) tagging is the process of assigning to each word in a text its corresponding grammatical information POS. It is an important pre-processing step in other natural language processing (NLP) tasks, so the objective of finding the most accurate one. The previous approaches were based on traditional machine learning algorithms, later with the development of deep learning, more POS taggers were adopted. If the accuracy of POS tagging reaches 97%, even with the traditional machine learning, for high resourced language like English, French, it’s far the case in low resource language like Amazigh. The most used approaches are traditional machine learning, and the results are far from those for rich language. In this paper, we present a new POS tagger based on bidirectional long short-term memory for Amazigh language and the experiments that have been done on real dataset shows that it outperforms the existing machine learning methods.
Backbone search for object detection for applications in intrusion warning systems Thuan, Nguyen Duc; Huong, Nguyen Thi Lan; Hong, Hoang Si
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1129-1138

Abstract

In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset. 

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