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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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Securing high-value electronic equipment: an internet of things driven approach for camera security Nordin, Siti Aminah; Yusoff, Zakiah Mohd; Hanif Faisal, Muhammad; Kamarudin, Khairul
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.pp2763-2772

Abstract

This article addresses pressing challenge of securing high-value electronic equipment, notably cameras, which face dual threats of damage in high humidity conditions and theft due to their significant market value. To confront these issues, the study introduces innovative internet of things (IoT)-driven approach aimed at strengthening conventional storage box security. Central to this approach is integration of IoT technologies, such as Arduino and ESP32, to develop advanced safety storage box. This enhanced system features essential hardware components, including buzzer, password-protected keypad, radio frequency identification reader, and DHT11 sensor for humidity monitoring. Additionally, mobile alarm system is incorporated to promptly alert owners of any detected movement vibrations, thereby augmenting security measures. By leveraging these components, proposed methodology seeks to mitigate risks associated with camera theft and fungal contamination, thereby advancing electronic device security. The expected outcome is marked enhancement in protection of high-value electronic equipment, particularly cameras, through continuous real-time monitoring and proactive security measures. This research underscore’s critical role of IoT technologies in fortifying security measures for valuable electronic assets, contributing significantly to ongoing discourse on innovative strategies in field. Through its comprehensive approach, this study aims to offer practical solutions to mitigate security risks and safeguard electronic equipment against potential threats, thereby addressing critical need in realm of electronic device security.
Improved performance of fake account classifiers with percentage overlap features selection Tjahyanto, Aris; Pratama, Rivanda Putra; Shiddiqi, Ary Mazharuddin
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.pp1585-1595

Abstract

Feature selection plays a crucial role in the development of high-performance classification models. We propose an innovative method for detecting fake accounts. This method leverages the percentage overlap technique to refine feature selection. We introduce our technique upon earlier work that showcased the enhanced efficacy of the Naïve Bayesian classifier through dataset normalization. Our study employs a dataset of account profiles sourced from Twitter, which we normalize using the Min-Max method. We analyze the results through a series of comprehensive experiments involving diverse classification algorithms—such as Naïve Bayes, decision tree, k-nearest neighbors (KNN), deep learning, and support vector machines (SVM). Our experimental results demonstrate a 100% accuracy achieved by the SVM and deep learning classifiers. The results are attributed to the percentage overlap technique, which facilitates the identification of four highly informative features. These findings outperform models with more extensive feature sets, underscoring the efficacy of our approach.
Review of early and accurate detection of Parkinson’s disease Mathew Biju, Soly; Al-Khatib, Obada; Chuan Lim, Hock; Malt, Suzanne; Zahid Sheikh, Hashir
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.pp3172-3187

Abstract

Parkinson’s disease (PD) is a central sensory system-based progressive illness with no cure. The origin of this illness is unknown. According to various research, it has been found that it is caused due to genetics or environmental factors. It is usually found in older people. However, there is no accurate treatment for this disease. So, the patient must be monitored periodically. It usually starts with deterioration in speech performance. The major problem with this disease is that it’s very costly to treat. The paper aims to report details of numerous aspects of detection of PD published in recent years based on the focus and benefits of the study, the methodology being used, accuracy of the system, and future research suggested for the study. A systematic study was done based on a search of the literature. A total of 50 articles were discovered.
A genetic algorithm-based feature selection approach for diabetes prediction Kangra, Kirti; Singh, Jaswinder
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.pp1489-1498

Abstract

Genetic algorithms have emerged as a powerful optimization technique for feature selection due to their ability to search through a vast feature space efficiently. This study discusses the importance of feature selection for prediction in healthcare and prominently focuses on diabetes mellitus. Feature selection is essential for improving the performance of prediction models, by finding significant features and removing unnecessary among them. The study aims to identify the most informative subset of features. Diabetes is a chronic metabolic disorder that poses significant health challenges worldwide. For the experiment, two datasets related to diabetes were downloaded from Kaggle and the results of both (datasets) with and without feature selection using the genetic algorithm were compared. Machine learning classifiers and genetic algorithms were combined to increase the precision of diabetes risk prediction. In the preprocessing phase, feature selection, machine learning classifiers, and performance metrics methods were applied to make this study feasible. The results of the experiment showed that genetic algorithm + logistic regression i.e., 80% (accuracy) works better for PIMA diabetes, and for Germany diabetes dataset genetic algorithm + random forest and genetic algorithm + K-Nearest Neighbor i.e., 98.5% performed better than other chosen classifiers. The researchers can better comprehend the importance of feature selection in healthcare through this study.
Barcode-less Fruits Classification Using Deep Learning Abdel-raouf, Amal; Sheta, Alaa; Baareh, AbdelKarim; Rausch, Peter
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.pp3211-3217

Abstract

Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.
Pneumonia prediction on chest x-ray images using deep learning approach Puspita, Rani; Rahayu, Cindy
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.pp467-474

Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest x-ray images. In deep learning, computers can automatically identify useful features for the model, directly from the raw data, bypassing the difficult step of manual information refinement. The main part of the deep learning method is the focus on automatically learning data representations. Visual geometry group 16 (VGG16) and DenseNet121 are methods in deep learning. The data used is a chest x-ray of pneumonia. Data is divided into training, testing, and validation. The best method for this research case is VGG16 with 93% accuracy training and 90% accuracy testing. In this study, DenseNet121 obtained accuracy below VGG16, with 92% accuracy in training and 88% for accuracy testing. Parameters have a significant influence on the accuracy of each model, and with the parameters that have been used, the VGG16 is a method that has high accuracy and can be used to predict chest x-ray images aimed at checking pneumonia in patients. 
Predicting the outcome of regional development projects using machine learning Satri, Jihad; El Mokhi, Chakib; Hachimi, Hanaa
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.pp863-875

Abstract

Morocco, in its pursuit of inclusive and sustainable territorial development, initiated the advanced regionalization experiment over six years ago. The primary challenge facing government officials today is the management of a burgeoning number of regional development projects. In this article we developed a predictive model based on artificial intelligence and Machine Learning to predict the outcomes of regional development projects, in order to identify the risks associated with their potential failure, and anticipate their impact. To accomplish this, we implemented various data mining techniques and classification algorithms. We collected and analyzed data from past and ongoing regional development projects, considering diverse factors that influence their success or failure. Through rigorous experimentation, we assessed the effectiveness of different predictive models. Our findings reveal that the Random Forest classifier stands out as an efficient algorithm for predicting the outcomes of regional development projects. This research contributes to the broader discourse on the practical implementation of artificial intelligence in public policy and regional development, showcasing its potential to optimize resource allocation, and alleviate the burden of repetitive administrative tasks for organizationsoperating with limited resources.
New insight in cervical cancer diagnosis using convolution neural network architecture Khozaimi, Ach; Firdaus Mahmudy, Wayan
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.pp3092-3100

Abstract

The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.
Towards a Docker-based architecture for open multi-agent systems Lima, Gustavo Lameirão de; Aguiar, Marilton Sanchotene de
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.pp45-56

Abstract

In open multi-agent systems (OMAS), heterogeneous agents in different environments or models can migrate from one system to another, taking their attributes and knowledge and increasing developing complexity compared to conventional multi-agent systems (MAS). Furthermore, the complexity of opening may be due to the uncertainties and dynamic behavior that the change of agents entails, needing to formulate techniques to analyze this complexity and understand the system’s global behavior. We used Docker to approach these problems and make the architecture flexible to handle distinct types of programming languages and frameworks of agents. This paper presents a Docker-based architecture to aid OMAS development, acting on agent migration between different models running in heterogeneous hardware and software scenarios. We present a simulation scenario with NetLogo’s Open Sugarscape 2 Constant Growback and JaCaMo’s Gold Miners to verify the proposal’s feasibility.
Towards a disease prediction system: biobert-based medical profile representation Hatoum, Rima; Alkhazraji, Ali; Ibrahim, Zein Al Abidin; Dhayni, Houssein; Sbeity, Ihab
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.pp2314-2322

Abstract

Predicting diseases in advance is crucial in healthcare, allowing for early intervention and potentially saving lives. Machine learning plays a pivotal role in healthcare advancements today. Various studies aim to predict diseases based on prior knowledge. However, a significant challenge lies in representing medical information for machine learning. Patient medical histories are often in an unreadable format, necessitating filtering and conversion into numerical data. Natural language processing (NLP) techniques have made this task more manageable. In this paper, we propose three medical information representations, two of which are based on bidirectional encoder representations from transformers for biomedical text mining (BioBERT), a state-of-the-art text representation technique in the biomedical field. We compare these representations to highlight the powerful advantages of BioBERT-based methods in disease prediction. We evaluate our approach efficiency using the medical information mart for intensive careIII (MIMIC-III) database, containing data from 46,520 patients. Our focus is on predicting coronary artery disease. The results demonstrate the effectiveness of our proposal. In summary, BioBERT, NLP techniques, and the MIMIC-III database are key components in our work, which significantly enhances disease prediction in healthcare.

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