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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
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Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity Arifudin, Riza; Subhan, Subhan; Ifriza, Yahya Nur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1773-1781

Abstract

Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.
A framework of attribute extraction and dependable aspect term selection from reviews of hospital websites Mohammed Basha, Nasreen Taj; Gowdra Shivappa, Girisha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3456-3465

Abstract

Online reviews found on hospital websites and external platforms constitute user-generated content where patients and their families share their firsthand encounters. As patients increasingly rely on online platforms to share their experiences, understanding the importance of their feedback is paramount for healthcare providers. The novelty of this research lies in the development of advanced frameworks that not only extract relevant information but also offer a more sophisticated and coherent analysis of the multifaceted aspects embedded in patient reviews. Hence, this work involves collecting data from various hospital websites, followed by data pre-processing to ensure accuracy and consistency. Subsequently, two distinct frameworks are proposed. The first framework aims to extract specific attributes (topics) mentioned in reviews, enhancing the granularity of information derived from the collected data. The second framework addresses the efficient extraction of aspect terms from pre-processed data, utilizing a coherence score-based approach called as modified latent dirichlet allocation term frequency-inverse document frequency (M-LDA TF-IDF). The M-LDA TF-IDF has achieved better a coherence score of 0.478 which is much better in comparison with other topic modelling approaches.
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%.
Generative adversarial network-based phishing URL detection with variational autoencoder and transformer Kaitholikkal Sasi, Jishnu; Balakrishnan, Arthi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2165-2172

Abstract

Phishing attacks pose a constant threat to online security, necessitating the development of efficient tools for identifying malicious URLs. In this article, we propose a novel approach to detect phishing URLs employing a generative adversarial network (GAN) with a variational autoencoder (VAE) as the generator and a transformer model with self-attention as the discriminator. The VAE generator is trained to produce synthetic URLs. In contrast, the transformer discriminator uses its self-attention mechanism to focus on the different parts of the input URLs to extract crucial features. Our model uses adversarial training to distinguish between legitimate and phishing URLs. We evaluate the effectiveness of the proposed method using a large set of one million URLs that incorporate both authentic and phishing URLs. Experimental results show that our model is effective, with an impressive accuracy of 97.75%, outperforming the baseline models. This study significantly improves online security by offering a novel and highly accurate phishing URL detection method.
An innovative approach for detecting buildings and construction anomalies in Zenata City Ait Moulay, Maryem; Salbi, Adil; Bouganssa, Issam; Masmoudi, Mohamed-Salim; Lasfar, Abdelali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2703-2712

Abstract

Rapid urban development in Morocco has led to increased construction activities and significant environmental concerns. Recently Zenata city has undergone significant urban development, marking a crucial step in its trajectory toward a modern smart city. As a part of this growth, our research incorporates an innovative method within the You Only Look Once version 8 (YOLOv8) model, representing a significant advance over conventional methods. The YOLO algorithm has been updated with new features and improvements that infuse our work with a dash of innovation. YOLOv8 integration improves construction and irregular construction detection accuracy beyond what is possible with traditional applications. We trained our algorithm using orthophoto captured by DJI MATRICE 300 RTK drone split into georeferenced tiles and annotated using LabelImg software. Through this process, we were able to create a solid 742 image dataset for training, testing, and validation purposes related to construction. Utilizing drone imagery and the YOLOv8 object detection algorithm, buildings and construction irregularities are detected with high accuracy after 300 training epochs on Kaggle's GPU P100. Insights for early detection and effective building site management are provided by this all-encompassing strategy, which supports Zenata City's sustainable urban growth. 
Coronavirus risk factor by Sugeno fuzzy logic Qasim Hasan, Saba; Omar Al-Nima, Raid Rafi; Esmail Mahmmod, Sahar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1420-1429

Abstract

World recently faced big challenges with the pandemic of coronavirus disease 2019 (COVID-19). Governments suffer from the problem of appropriately identifying the risk factor of this virus and establishing their safety procedures accordingly. This paper concentrates on designing a coronavirus risk factor (CRF) by the power of Sugeno fuzzy logic (SFL). The main advantage of the CRF is that it can provides a quick and suitable risk evaluation. According to the degree of severity, three essential parameters are considered: number of infected cases, number of people in intensive care units (ICU) and number of deaths. All of these parameters are provided per population. Such interesting and promising outcomes are attained, where the total effect is found equal to 95.3%.
Financial technology forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior Al-Khowarizmi, Al-Khowarizmi; Watts, Michael J.; Efendi, Syahril; Abdulbasah Kamil, Anton
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2386-2394

Abstract

Financial technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. FinTech can make any transactions anywhere with the pillars of peer-to-peer (P2P) lending, merchants, and crowdfunding. In the P2P lending pillar, there are borrowers and lenders who are digitized in FinTech devices. FinTech in Indonesia is controlled by a state agency called the financial services authority or otoritas jasa keuangan (OJK). In the movement of P2P lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as evolving connectionist system (ECoS), which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a mean absolute percentage error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1=0.6 and a learning rate 2=0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders.
Hand gesture-based automatic door security system using squeeze and excitation residual networks Prihanto, Surya; Effendy, Nazrul; Nopriadi, Nopriadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1619-1624

Abstract

Viruses can be transmitted in various ways; one spreads through airborne droplets or the touch of multiple objects. This can occur in any area, including the entrance to the house or access to a room or deposit box. The spread of viruses that cause diseases like COVID-19 has caused many human casualties, and there is still the possibility of similar conditions appearing in the future. Several things need to be done to reduce the chances of spreading disease due to viruses, including developing contactless security support methods. This paper proposes a security system using hand gesture recognition using squeeze and excitation residual networks (SE-ResNet). This research offers a hand gesture recognition system for an automatic door system using SE-ResNet and the residual network (ResNet).
Framework for contextual consulting practices in adherence for decentralized data-driven decision making Pandey, Vijay Kumar; Rathore, Neeraj; Bhosale, Narayan P.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2546-2556

Abstract

With the rising adoption of technological advancement and industry-based automation standards, the area of consulting firms is gradually evolving to keep up this pace towards incorporating sophisticated analytical operation for facilitating value-added consulting services. Review of existing practices of consulting firm shows increasing adoption of analytical process which leads to complex form of operation towards knowledge discovery of consulting data. Hence, this manuscript introduces a framework of contextual consulting practices where the core idea is to incorporate a baseline structure of knowledge discovery associated with consulting data in adherence of industry 4.0 automation standards. The framework takes the input of streamed consulting diversified data governed by a template-based entry-points where the consulting data is subjected to series of transformation operation that not only preprocess the consulting data but also optimizes the data to enhance its data quality. The study model is implemented in MATLAB considering an extensive analytical framework towards data-driven decision making and decentralization to exhibit proposed model to offer better analytical performance in contrast to existing study models.
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.

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