cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 50 Documents
 1   2   3   4   5 
Search results for , issue "Vol 12, No 2: June 2023" : 50 Documents clear
A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays Syifaul Fuada; Trio Adiono; Hans Kasan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp714-730

Abstract

In respect of the accuracy, one of the well-known techniques for human detection is the histogram-oriented gradients (HOG) method. Unfortunately, the HOG feature calculation is highly complex and computationally intensive. Thus, in this research, we aim to achieve a resource-efficient and low-power HOG hardware architecture while maintaining its high frame-rate performance for real-time processing. A hardware architecture for human detection in 2D images using simplified HOG algorithm was introduced in this paper. To increase the frame-rate, we simplify the HOG computation while maintaining the detection quality. In the hardware architecture, we design a cell-based processing method instead of a window-based method. Moreover, 64 parallel and pipeline architectures were used to increase the processing speed. Our pipeline architecture can significantly reduce memory bandwidth and avoid any external memory utilization. an altera field programmable gate arrays (FPGA) E2-115 was employed to evaluate the design. The evaluation results show that our design achieves performance up to 86.51 frame rate per second (Fps) with a relatively low operating frequency (27 MHz). It consumes 48,360 logic elements (LEs) and 4,363 registers. The performance test results reveal that the proposed solution exhibits a trade-off between Fps, clock frequency, the use of registers, and Fps-to-clock ratio.
Architecting a machine learning pipeline for online traffic classification in software defined networking using spark Sama Salam Samaan; Hassan Awheed Jeiad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp861-873

Abstract

Precise traffic classification is essential to numerous network functionalities such as routing, network management, and resource allocation. Traditional classification techniques became insufficient due to the massive growth of network traffic that requires high computational costs. The arising model of software defined networking (SDN) has adjusted the network architecture to get a centralized controller that preserves a global view over the entire network. This paper proposes a model for SDN traffic classification based on machine learning (ML) using the Spark framework. The proposed model consists of two phases; learning and deployment. A ML pipeline is constructed in the learning phase, consisting of a set of stages combined as a single entity. Three ML models are built and evaluated; decision tree, random forest, and logistic regression, for classifying a well-known 75 applications, including Google and YouTube, accurately and in a short time scale. A dataset consisting of 3,577,296 flows with 87 features is used for training and testing the models. The decision tree model is elected for deployment according to the performance results, which indicate that it has the best accuracy with 0.98. The performance of the proposed model is compared with the state-of-the-art works, and better accuracy result is reported.
Automated invoice data extraction using image processing Akanksh Aparna Manjunath; Manjunath Sudhakar Nayak; Santhanam Nishith; Satish Nitin Pandit; Shreyas Sunkad; Pratiba Deenadhayalan; Shobha Gangadhara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp514-521

Abstract

Manually processing invoices which are in the form of scanned photocopies is a time-consuming process. There is a need to automate the task of extraction of data from the invoices with a similar format. In this paper we investigate and analyse various techniques of image processing and text extraction to improve the results of the optical character recognition (OCR) engine, which is applied to extract the text from the invoice. This paper also proposes the design and implementation of a web enabled invoice processing system (IPS). The IPS consists of an annotation tool and an extraction tool. The annotation tool is used to mark the fields of interest in the invoice which are to be extracted. The extraction tool makes use of opensource computer vision library (OpenCV) algorithms to detect text. The proposed system was tested on more than 25 types of invoices with the average accuracy score lying between 85% and 95%. Finally, to provide ease of use, a web application is developed which also presents the results in a structured format. The entire system is designed so as to provide flexibility and automate the process of extracting details of interest from the invoices.
Deep convolutional neural networks-based features for Indonesian large vocabulary speech recognition Hilman F. Pardede; Purwoko Adhi; Vicky Zilvan; Ade Ramdan; Dikdik Krisnandi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp610-617

Abstract

There are great interests in developing speech recognition using deep learning technologies due to their capability to model the complexity of pronunciations, syntax, and language rules of speech data better than the traditional hidden Markov model (HMM) do. But, the availability of large amount of data is necessary for deep learning-based speech recognition to be effective. While this is not a problem for mainstream languages such as English or Chinese, this is not the case for non-mainstream languages such as Indonesian. To overcome this limitation, we present deep features based on convolutional neural networks (CNN) for Indonesian large vocabulary continuous speech recognition in this paper. The CNN is trained discriminatively which is different from usual deep learning implementations where the networks are trained generatively. Our evaluations show that the proposed method on Indonesian speech data achieves 7.26% and 9.01% error reduction rates over the state-of-the-art deep belief networks-deep neural networks (DBN-DNN) for large vocabulary continuous speech recognition (LVCSR), with Mel frequency cepstral coefficients (MFCC) and filterbank (FBANK) used as features, respectively. An error reduction rate of 6.13% is achieved compared to CNN-DNN with generative training.
Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction Sri Kusumadewi; Linda Rosita; Elyza Gustri Wahyuni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp532-542

Abstract

It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.
Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application Shouket Abdulrahman Ahmed; Hazry Desa; Abadal-Salam T. Hussain
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp641-647

Abstract

The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.
Multi-objective load balancing in cloud infrastructure through fuzzy based decision making and genetic algorithm based optimization Neema George; Anoop Balakrishnan Kadan; Vinodh P. Vijayan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp678-685

Abstract

Cloud computing became a popular technology which influence not only product development but also made technology business easy. The services like infrastructure, platform and software can reduce the complexity of technology requirement for any ecosystem. As the users of cloud-based services increases the complexity of back-end technologies also increased. The heterogeneous requirement of users in terms for various configurations creates different unbalancing issues related to load. Hence effective load balancing in a cloud system with reference to time and space become crucial as it adversely affect system performance. Since the user requirement and expected performance is multi-objective use of decision-making tools like fuzzy logic will yield good results as it uses human procedure knowledge in decision making. The overall system performance can be further improved by dynamic resource scheduling using optimization technique like genetic algorithm.
Artificial intelligence: the major role it played in the management of healthcare during COVID-19 pandemic Tabrez Uz Zaman; Elaf Khalid Alharbi; Aeshah Salem Bawazeer; Ghala Abdullah Algethami; Leen Abdullah Almehmadi; Taif Muhammed Alshareef; Yasmin Awwadh Alotaibi; Hosham Mohammed Osman Karar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp505-513

Abstract

The sudden arrival of COVID-19 called for new technologies to manage the healthcare system and to reduce the burden of patients in the hospitals. Artificial intelligence (AI) which involved using computers to model intelligent behavior became an important choice. Various AI applications helped a lot in the management of healthcare and delivering quick medical consultations and various services to a wide variety of patients. These new technological developments had significant roles in detecting the COVID-19 cases, monitoring them, and forecasting for the future. Artificial intelligence is applied to mimic the functional system of human intelligence. AI techniques and applications are also applied in proper examinations, prediction, analyzing, and tracking of the whereabouts of patients and the projected results. It also played a significant role in recognizing and proposing the generation of vaccines to prevent COVID-19. This study is therefore an attempt to understand the major role and use of AI in healthcare institutions by providing urgent decision-making techniques that greatly helped to manage and control the spread of the COVID-19 disease.
K-means clustering analysis and multiple linear regression model on household income in Malaysia Gan Pei Yee; Mohd Saifullah Rusiman; Shuhaida Ismail; Suparman Suparman; Firdaus Mohamad Hamzah; Muhammad Ammar Shafi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp731-738

Abstract

Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DoSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.
Using skeleton model to recognize human gait gender Omar Ibrahim Alsaif; Saba Qasim Hasan; Abdulrafa Hussain Maray
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp974-983

Abstract

Biometrics became fairly important to help people identifications persons by their individualities or features. In this paper, gait recognition has been based on a skeleton model as an important indicator in prevalent activities. Using the reliable dataset for the Chinese Academy of Sciences (CASIA) of silhouettes class C database. Each video has been discredited to 75 frames for each (20 persons (10 males and 10 females)) as (1.0), the result will be 1,500 frames. After Pre-processing the images, many features are extracted from human silhouette images. For gender classification, the human walking skeleton used in this study. The model proposed is based on morphological processes on the silhouette images. The common angle has been computed for the two legs. Later, principal components analysis (PCA) was applied to reduce data using feature selection technology to get the most useful information in gait analysis. Applying two classifiers artificial neural network (ANN) and Gaussian Bayes to distinguish male or female for each classifier. The experimental results for the suggested method provided significant accomplishing about (95.5%), and accuracy of (75%). Gender classification using ANN is more efficient from the Gaussian Bayes technique by (20%), where ANN technique has given a superior performance in recognition.

Page 2 of 5 | Total Record : 50

 1   2   3   4   5 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue