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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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Real-time indoor tracking for augmented reality using computer vision technique Shewail, Ashraf Saad; H. Zayed, Hala; A. M. Elsayed, Neven
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.pp1845-1857

Abstract

In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces
TMS320F28379D microcontroller for speed control of permanent magnet direct current motor Chalardsakul, Tanawat; Piliyasilpa, Chotnarin; Sukontanakarn, Viroch
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.pp2816-2828

Abstract

This paper aims to study the behavior of the proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies the controlling voltage based on motor speed errors. Finally, the result shows the fuzzy-based tuning PI-D controller approach has a minimum overshoot, and minimum transient and steady state parameters compared to the PID controller to control the speed of the motor. The PID controllers have poorer performance due to the non-linear features of the PMDC motor.
Enhancing accessibility and discoverability of digital archive images through automated image recognition tool Thammastitkul, Akara; Petsuwan, Jitsanga
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.pp1294-1303

Abstract

This research paper presents a comprehensive evaluation of the effectiveness of Imagga and Google cloud vision application programming interface (API) as image recognition tools for generating metadata in digital archive images. The assessment encompasses a diverse range of archive images, including those without text, images with text, and both color and black-and-white images. Through the use of evaluation metrics such as cosine similarity, word overlap similarity, recall, precision, and F1 score, the performance of these tools is quantitatively measured. The findings highlight the strong individual performance of both Imagga and Google cloud vision API, with the combined metadata outputs achieving significantly higher scores across all metrics. This emphasizes the potential benefits of employing a combined approach, leveraging the strengths of multiple tools to enhance the reliability and robustness of the metadata extraction process. The findings contribute to the advancement of metadata management in digital archives and underscore the importance of utilizing multiple tools for improved performance in image metadata generation.
Herbal plant leaves classification for traditional medicine using convolutional neural network Fauzi, Alfharizky; Soerowirdjo, Busono; Haryatmi, Emy
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.pp3322-3329

Abstract

The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.
A novel ensemble model for detecting fake news Bensouda, Nissrine; El Fkihi, Sanaa; Faizi, Rdouan
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.pp1160-1171

Abstract

Due the growing proliferation of fake news over the past couple of years, ourobjective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we optfor a blending technique that combines three models, namely bidirectional longshort-term memory (Bi-LSTM), stochastic gradient descent classifier and ridgeclassifier. The implementation of the proposed model (i.e. BI-LSR) on realworld datasets, has shown outstanding results. In fact, it achieved an accuracyscore of 99.16%. Accordingly, this ensemble learning has proven to do performbetter than individual conventional machine learning and deep learning modelsas well as many ensemble learning approaches cited in the literature.
Framework for content server placement using integrated learning in content delivery network Dharmapal, Priyanka; Channakrishnaraju, Channakrishnaraju
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.pp3028-3038

Abstract

Content placement is a significant concern in content delivery networks (CDN), irrespective of various evolving studies. Existing methodologies showcase various significant unaddressed issues concerning content placement approaches' complexities. Therefore, the proposed study presents a novel computational framework towards dynamic content placement strategy using a novel integrated machine learning approach. Simplified mathematical modelling is used to formulate and solve the content placement problem. At the same time, reinforcement learning and the sequential attentional neural network have been utilized to optimize the decision-making towards placement of content servers. Designed and assessed over a Python environment, the proposed scheme is witnessed to exhibit 35% reduced bandwidth utilization, 20% reduced delay, 23% reduced computational resource utilization, and 28% reduced algorithm processing time in contrast to existing predictive content placement schemes.
Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia Azhar, Daris; Kurniawan, Robert; Marsisno, Waris; Yuniarto, Budi; Sukim, Sukim; Sugiarto, Sugiarto
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.pp375-382

Abstract

The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) architecture because it can produce a good performance and only requires a relatively short computation time. Meanwhile, the baseline NER model uses the bidirectional long short-term memory-conditional random field (Bi-LSTMs-CRF) architecture because it is easy to implement using the Flair framework. The primary model that has been built results in a performance (F1 score) of 82.54%. Meanwhile, the baseline model only results in a performance (F1 score) of 69.67%. Then, the raw data extracted by NER is processed to produce the number of drug suspects in Indonesia from 2018-2020. However, the data that has been produced is not as complete as similar data sourced from Indonesian National Narcotics Board publications.
Fuzzified input data tuning for agriculture commodities price prediction Hegde, Girish; Hulipalled, Vishwanath R.; Simha, Jay B.
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.pp2725-2735

Abstract

The quality of the input data will typically affect the prediction accuracy. Preprocessing of data is commonly referred to as input data tuning. Tuning the input data is critical for projecting commodity prices. Anomalies or outliers are unavoidable in historical price data. To increase prediction and forecasting accuracy, it is necessary to find and correct outliers before training the prediction model. To correct the anomaly and increase prediction accuracy, the Fuzzified Input Data Tuning and Prediction algorithm proposed in this study. The identified outliers are corrected using the relevant fuzzy set value in this method. With outlier corrected data, we used Long Short-Term Memory and Seasonal Autoregressive Integrated Moving Average to anticipate tomato prices in Karnataka state. The result of the proposed algorithm is compared with the Sliding Window anomalies correction model, and without disposing of the outliers. The suggested algorithm, with 37.89%, performed better than Sliding Window with 40.08% and 43.11% Mean Absolute Percentage Error, respectively, and without outlier correction. The sensitivity analysis shows that the performance of the model is unaffected by the forecasting horizon. Finally, comparitive analysis peformed with previous research work, and the proposed model performed better.
Optimized deep learning-based dual segmentation framework for diagnosing health of apple farming with the internet of things Raju, Harsha; Narasimhaiah, Veena Kalludi
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.pp876-887

Abstract

The high disease prevalence in apple farms results in decreased yield and income. This research addresses these issues by integrating internet of things (IoT) applications and deep neural networks to automate disease detection. Existing methods often suffer from high false positives and lack global image similarity. This study proposes a conceptual framework using IoT visual sensors to mitigate apple diseases' severity and presents an intelligent disease detection system. The system employs the augmented Otsu technique for region-aware segmentation and a colour-conversion algorithm for generating feature maps. These maps are input into U-net models, optimized using a genetic algorithm, which results in the generation of suitable masks for all input leaf images. The obtained masks are then used as feature maps to train the convolution neural network (CNN) model for detecting and classifying leaf diseases. Experimental outcomes and comparative assessments demonstrate the proposed scheme's practical utility, yielding high accuracy and low false-positive results in multiclass disease detection tasks.
Image translation between human face and wayang orang using U-GAT-IT Nurdenara, Ciara; Fawwaz Al Maki, Wikky
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.pp2451-2458

Abstract

Wayang orang performance is one of the Indonesian traditional cultures. The wayang orang players took about an hour to become a proper wayang orang since it takes time to have makeup and to find the appropriate costume before the performance is held. This problem can be solved by developing a computer-based simulation on applying makeup and traditional costume to the face and head of the wayang orang player, respectively. This task can be completed by using image translation. Therefore, people's images can be transformed into wayang orang images. This study aims to translate human faces into wayang orang by adding makeup and accessories using the U-GAT-IT with an unpaired dataset consisting of 1216 data trains and 240 data tests. The challenge of this research is to maintain the image background and the facial identity component in the input image. This research employs quantitative testing employ Kernel Inception Distance (KID), Frèchet Inception Distance (FID), and Inception Score (IS) to evaluate the quality of the output image obtained from the generator. The experimental results show that U-GAT-IT produces a better result than DCLGAN does according to the value of IS, FID, and KID. The IS, FID, and KID obtained by implementing U-GAT-IT are 2.414, 0.924, and 4.357, respectively.

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