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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 121 Documents
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Search results for , issue "Vol 13, No 3: September 2024" : 121 Documents clear
Enhancing internet of things security and efficiency through advanced elliptic curve cryptography-based strategies in fog computing Srinivasa Ravindra, Krishnapura; Panduranga Rao, Malode Vishwanatha
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.pp3523-3532

Abstract

Fog computing (FC) has evolved as a significant paradigm within the internet of things (IoT) ecosystem, serving as a crucial link between edge devices and centralised cloud computing resources. This research paper investigates advanced methodologies for improving the security and efficiency of FC in the IoT domain. The primary emphasis is placed on the utilisation of elliptic curve cryptography (ECC) to accomplish these goals. This study examines the difficulties encountered in ensuring the security of IoT deployments based on FC. It also presents novel solutions based on ECC to mitigate these obstacles. Moreover, this study investigates techniques for enhancing the efficiency and allocation of resources in IoT applications within a FC environment. This study seeks to offer significant insights into the application of ECC-based techniques for enhancing the security and efficiency of FC in the context of the IoTs. These insights are derived through a combination of theoretical analysis and practical implementations. To evaluate the effectiveness of the proposed system, an analysis is conducted to examine the encryption time, decryption time, and correlation coefficients. These metrics are then compared to those of existing state-of-the-art approaches.
A design of a brain tumor classifier of magnetic resonance imaging images using ResNet101V2 with hyperparameter tuning Maulana Zein, Rhendiya; Effendy, Nazrul; Basuki, Endro; Nopriadi, Nopriadi
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.pp3141-3146

Abstract

Brain tumors are a disease that is quite dangerous and requires severe treatment. One thing that is quite important is the process of diagnosing the brain tumor. This diagnosis process requires intense attention, and differences in interpretation may arise. Machine learning has been used in several fields, including disease diagnosis. This paper proposes an intelligent diagnostic tool for brain tumors using ResNet101v2. ResNet101V2 is used to classify meningioma, glioma, pituitary, and normal from magnetic resonance imaging (MRI) images. This research includes data collection, data preprocessing, ResNet101v2 design and evaluation. We investigate three models of ResNet101v2 for brain tumor classification. The best model achieves an accuracy of 96.2%.
A comparison of meta-heuristic and hyper-heuristic algorithms in solving an urban transit routing problems Muklason, Ahmad; Ahlan Robbani, Shof Rijal; Riksakomara, Edwin; Premananda, I Gusti Agung
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.pp2923-2933

Abstract

Public transport is a serious problem that is difficult to solve in many countries. Public transport routing optimization problem also known as urban transit routing problem (UTRP) is time-consuming process, therefore effective approches are urgently needed. UTRP aims to minimize cost passenger and operator from a combination of route set. UTRP can be optimize with heuristics, meta-heuristics, and hyper-heuristics methods. In several previous studies, UTRP can be optimized with any meta-heuristics and hyper-heuristics methods. In this study we compare the performance of meta-heuristic methods, i.e. ill-climbing, simulated annealing, and hyper-heuristics method based on modified particle swarm optimization algorithm. The experimental results showed that the proposed methods could solve UTRP effectively. Regarding their performance, the results show that despite the generality of hyper-heuristics, their performance are competitive. More specifically, hyper-heuristics method is the best method compared to the other two methods in each dataset. In addition, compared to prior studies results, he proposed hyper-heuristics could outperform them in term of cost passenger of small dataset Mandl. The main contribution of this paper is that to best of our knowledge, it is the first study comparing the performance of meta-heuristics and hyper-heuristics approaches over UTRP.
Enhancing stroke prediction using the waikato environment for knowledge analysis Altayeb, Muneera; Arabiat, Areen
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.pp3010-3017

Abstract

State-of-the-art data mining tools incorporate advanced machine learning (ML) and artificial intelligence (AI) models, and it is widely used in classification, association rules, clustering, prediction, and sequential models. Data mining is important for the process of diagnosing and predicting diseases in the early stages, and this contributes greatly to the development of the health services sector. This study utilized classification to predict the stroke of a sample of the patient dataset that was taken from Kaggle. The classification model was created using the data mining program waikato environment for knowledge analysis (WEKA). This data mining tool helped identify individuals most at risk of stroke based on analysis of features extracted from the patient’s dataset. These features were used in classification processes according to the naive Bayes (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. Analysis of the classification results of the previous algorithms showed that the SVM outperformed other algorithms in terms of accuracy (94.4%), sensitivity (100%), and F-measure (97.1%). However, the NB algorithm had the best performance in terms of precision (95.7%).
You only look once v8 for fish species identification Rizqi Basuki, Nurfadjri Akbar; Hustinawaty, Hustinawaty
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.pp3314-3321

Abstract

This research aims to test the performance of you only look once (YOLOv8) in identifying fish species in Indonesian waters. Fish image data is obtained from various sources to conduct testing. The results show that YOLOv8 is able to identify fish species with a mAP accuracy rate of 97%. These results reveal the great potential of deep learning technology in supporting the preservation of marine biodiversity as well as the development of various applications, such as fisheries monitoring, conservation, and marine-based tourism development in Indonesia. With its efficient object detection and classification capabilities, YOLOv8 can simplify and accelerate the process of identifying fish species, even on a large scale. Thus, this technology is a highly effective solution to overcome the challenges of manual fish species identification, which requires a lot of time and effort. The results of this study provide valuable insights into the potential utilization of Indonesia's natural resources in the context of scientific development, the tourism industry, and the fisheries sector, which is vital to the country's economy.
Data-driven farming: implementing internet of things for agricultural efficiency Ismail Lafta, Mohamed; Dawood Abdullah, Wisam
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.pp3588-3598

Abstract

Integrating internet of things (IoT) technology into agriculture has become essential to address challenges such as low crop productivity, which is often due to insufficient soil nutrients and suboptimal environmental conditions. This paper discusses the design and implementation of an IoT-based system for agriculture that aims to automate key parameters, facilitate real-time monitoring, and promote sustainable practices. Equipped with a graphical user interface (GUI), the system focuses on improving irrigation, regulating temperatures, and correcting soil nutrient deficiencies to improve crop productivity. Our research includes the use of humidity sensors to monitor soil moisture and temperature sensors. Soil nutrient levels, especially nitrogen, phosphorus, and potassium (NPK), were also assessed. We conducted experiments on three radish varieties using this IoT system and compared the results with traditional farming methods. The germination rate was impressive, reaching 98% within the first four days, while in a traditional greenhouse, it did not exceed 50%. As for plant height and leaf area, the smart greenhouse was better. These results were promising and demonstrated the potential of IoT in enhancing agricultural productivity. These results highlight the significant impact of IoT technology in enhancing agricultural productivity and its potential for broader application in this sector.
Computer vision that can ‘see’ in the dark Goh, Shi Yong; Wong, Yan Chiew ; Ahmad Radzi, Syafeeza; Sarban Singh, Ranjit Singh
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.pp2883-2892

Abstract

Insufficient lighting environment has raised challenges for night shift workers’ safety monitoring. Thus, we have developed a computer vision-based algorithm recognizing 11 actions based on action recognition in dark (ARID) dataset. A hybrid model of integrating convolutional neural network (CNN) into YOLOv7 has been proposed. YOLOv7 is an algorithm designed for real-time object detection in image or video, for fast and accurate detection in applications such as autonomous vehicles and surveillance systems. In this work, video in dark environment has first been enhanced using CNN algorithm before feeding into YOLOv7 network for activity recognition. Adaptive gamma intensity correction (GIC) has been integrated to further improving the overall result. The proposed model has been evaluated over different enhancement modes. The proposed model is able to handle dark video frames with 74.95% Top-1 accuracy with fast processing speed of 93.99 ms/frame on a 4 GB RTX 3050 graphical processing unit (GPU) and 17.59 ms/frame on 16 GB Tesla T4 GPU. The base size of the proposed model is tiny, only 74.8 MB, but with 36.54 M of total parameters indicating that it has more capacity to learn more meaningful information with limited hardware resources.
Apple fruits categorizing based on deep convolutional neural network techniques Hussain, Nashaat; Zaki, Gihan; Hassan, Mohamed
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.pp3695-3702

Abstract

For a variety of reasons, including the high degree of similarity between varieties of the same type of fruit, the requirement to train the technique on a large amount of data, and the type and number of features suitable for application, the use of computer vision techniques in the classification of fruits still faces many challenges. Additionally, the technique's effectiveness and speed both need to be improved. Deep conventional neural network (DCNN) approaches were required for all of these reasons. A proposed CNN model is described in this work. The suggested methodology is intended to quickly and accurately categorize thirteen groups of apple fruits. The proposed technique was based on training and testing the model on a maximum number of images of apple fruits, by increasing the number of database images tenfold, after augmentation was performed on the images. The technology also relied on good tuning of the hyperparameters. To further ensure the efficiency of training, validation was performed on 20% of the database. All results that demonstrate the high efficiency of the proposed model were reviewed. The results of the proposal were compared with the results of four related techniques. The results showed the great advantage of the proposed technology at all levels.
DualFaceNet: augmentation consistency for optimal facial landmark detection and face mask classification Songsri-in, Kritaphat; Rattaphun, Munlika; Kaewchada, Sopee; Ruang-on, Somporn
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.pp3228-3239

Abstract

In an era where face masks are commonplace, facial recognition faces new challenges and opportunities. This study introduces DualFaceNet (DFN), a cutting-edge neural network that efficiently combines facial landmark detection with mask classification. Benefiting from multi-task learning (MTL) and enhanced with a unique consistency loss, DFN outperforms traditional single-task models. Tests using the reputable 300W dataset and a face mask dataset showcase DFN’s strengths: a significant reduction in landmark error to 5.42 and an increase in mask classification accuracy to 92.59%. These results highlight the potential of integrating MTL and custom loss functions in facial recognition. As face masks continue to be globally essential, DFN’s integrated approach offers a fresh perspective in facial recognition studies. Furthermore, DFN paves the way for adaptive facial recognition systems, emphasizing the adaptability needed in our current era.
Morphology for hexagonal image processing: a comprehensive simulation analysis Cevik, Taner; Nematzadeh, Sajjad; Rasheed, Jawad; Alshammari, Abdulaziz
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.pp2574-2590

Abstract

Morphological operators for binary and grayscale images are commonly used to eliminate noise, recognize contours or specific structures, and arrange shapes in image processing for physiological modeling and biomechanics applications. Even though morphology has been substantially developed in square-pixelbased-image-processing (SIP), no effort has been made to construct morphological operators in hexagonal-pixel-based-image-processing (HIP) yet. In this paper, we transform basic SIP-domain-morphological operators such as dilation, erosion, closing, and opening into HIP-domain and compare their performance with their SIP counterparts. It is the first time to give the fundamental morphological operators in the HIP domain. The operators developed in this paper initiate the research about morphology in the HIP domain by successfully filling a significant gap by eliminating HIP’s lack of basic operators, thus capable of producing enhanced images for better analysis in anatomical models related to biology and medicine research fields.

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