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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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A new approach for varied speed weigh-in-motion vehicle based on smartphone inertial sensors Ahmed A. Hamad; Yasseen Sadoon Atiya; Hilal Al-Libawy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Dynamic vehicle weight measuring, weigh-in-motion (WIM), is an important metric that can reflect significantly vehicle driving behaviour and in turn, it will affect both safety and traffic status. Several accurate (WIM) systems are developed and implemented successfully. These systems are using under road weighing sensor which are costly to implement. Moreover, it is costly and not very practical to embed a continuous weighing system in used cars. In this work, a low-cost varied-speed weigh-in-motion approach was suggested to continuously measuring vehicle load based on the response of smartphone sensors which is a reflection of vehicle dynamics. This approach can apply to any moving vehicle at any driving speed without the need for extra added hardware which makes it very applicable because smartphone is widely used device. The approach was tested through a six-trips experiment. Three capacities of load had been designed in this approach to be classified using a neural network classifier. The classification performance metrics are calculated and show an accuracy of 91.2%. This accuracy level is within error limits of existing WIM systems especially for high speed and proved the success of the suggested approach.
Classification technique for real-time emotion detection using machine learning models Chanathip Sawangwong; Kritsada Puangsuwan; Nathaphon Boonnam; Siriwan Kajornkasirat; Wacharapong Srisang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

This study aimed to explore models to identify a human by using face recognition techniques. Data were collected from Cohn-Kanade dataset composed of 398 photos having face emotion labeled with eight emotions (i.e., neutral, angry, disgusted, fearful, happy, sad, and surprised). Multi-layer perceptron (MLP), support vector machine (SVM), and random forest were used in model accuracy comparisons. Model validation and evaluation were performed using Python programming. The results on F1 scores for each class in the dataset revealed that predictive classifiers do not perform well for some classes. The support vector machine (RBF kernel) and random forest showed the highest accuracies in both datasets. The results could be used to extract and identify emotional expressions from the Cohn-Kanade dataset. Furthermore, the approach could be applied in other contexts to enhance monitoring activities or facial assessments. 
A linear regression approach to predicting salaries with visualizations of job vacancies: a case study of Jobstreet Malaysia Khyrina Airin Fariza Abu Samah; Nurqueen Sayang Dinnie Wirakarnain; Raseeda Hamzah; Nor Aiza Moketar; Lala Septem Riza; Zainab Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1130-1142

Abstract

This study explicitly discusses helping job seekers predict salaries and visualize job vacancies related to their future careers. Jobstreet Malaysia is an ideal platform for discovering jobs across the country. However, it is challenging to identify these jobs, which are organized according to their respective and specific courses. Therefore, the linear regression approach and visualization techniques were applied to overcome the problem. This approach can provide predicted salaries, which is useful as this enables job seekers to choose jobs more easily based on their salary expectations. The extracted Jobstreet data runs the pre-processing, develops the model, and runs on real-world data. A web-based dashboard presents the visualization of the extracted data. This helps job seekers to gain a thorough overview of their desired employment field and compare the salaries offered. The system’s reliability as tested using mean absolute error, the functionality test was performed according to the use case description, and the usability test was performed using the system usability scale. The reliability results indicate a positive correlation with the actual values. The functionality test produced a successful result, and a score of 96.58% was achieved for the system usability scale result, proving the system grade is ‘A’ and usable.
Sentinel-1A image classification for identification of garlic plants using decision tree and convolutional neural network Risa Intan Komaraasih; Imas Sukaesih Sitanggang; Annisa Annisa; Muhammad Asyhar Agmalaro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The Indonesian government launched a garlic self-sufficiency program by 2033 to reduce imports by monitoring garlic lands in several central garlic areas. Remote sensing using satellite imageries can assist the monitoring program by mapping the garlic lands. A previous study has classified Sentinel-1A satellite imageries to identify garlic lands in Sembalun Lombok Indonesia using the decision tree C5.0 algorithm with three scenarios data input and produced a model with an accuracy of 78.45% using scenarios with two attributes vertical-vertical (VV) and vertical-horizontal (VH) bands. Therefore, this study aims to improve the accuracy of the classification model from the previous study. This study applied two classification algorithms, decision tree C5.0 and convolutional neural network (CNN), with two new scenarios which used two new combinations of attributes). The results show that the use of new data scenarios as input for C5.0 can not increase the previous model's accuracy. While the use of the CNN algorithm shows that it can improve the previous study's accuracy by 7.91% because it produced a model with an accuracy of 86.36%. This study is expected to help garlic land identification in the Sembalun area to support government programs in monitoring garlic lands.
Predictive linguistic cues for fake news: a societal artificial intelligence problem Sandhya Aneja; Nagender Aneja; Ponnurangam Kumaraguru
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.
Reduced hardware requirements of deep neural network for breast cancer diagnosis Yasmine M. Tabra; Furat N. Tawfeeq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration on each subsystem to futher reduce the hardware requirements. The DNN was designed using a system generator and implemented using very hardware description language (VHDL). The system achievments outcomes the superior’s accuracy rate of approximately 99.6 percent in distinguishing bengin from malignant tissue. Also, the hardware resources were reduced by 30 percent from works of literature with an error rate of 7e-4 when using the Kintex-7 xc7k325t-3fbg676 board.
Fusion of Gabor filter and steerable pyramid to improve iris recognition system Mohamed Radouane; Nadia Idrissi Zouggari; Amine Amraoui; Mounir Amraoui
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Iris recognition system is a technique of identifying people using their distinctive features. Generally, this technique is used in security, because it offers a good reliability. Different researchers have proposed new methods for iris recognition system to increase its effectiveness. In this paper, we propose a new method for iris recognition based on Gabor filter and steerable pyramid decomposition. It’s an efficient and accurate linear multi-scale, multi-orientation image decomposition to capture texture details of an image. At first, the iris image is segmented, normalized and decomposed by Gabor filter and steerable pyramid method. Multiple sub-band are generated by applying steerable pyramid on the input image. High frequency sub-band is ignored to eliminate noise and increase the accuracy. The method was validated using CASIA-v4 (Chinese Academy of Sciences Institute of Automation), IITD (Indian Institute of Technology Delhi) and UPOL (University of Phoenix Online) databases. The performance of the proposed method is better than the most methods in the literature. The proposed algorithm provides accuracy of 99.99%. False acceptance rate (FAR), equal error rate (EER) and genuine acceptance rate (GAR) have also been improved.
Masters and Doctor of Philosophy admission prediction of Bangladeshi students into different classes of universities Md Naimul Islam Suvon; Sadman Chowdhury Siam; Mehebuba Ferdous; Mahabub Alam; Riasat Khan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Many Bangladeshi students intend to pursue higher studies abroad after completing their undergraduate degrees every year. Choosing a university for higher education is a challenging task for students. Especially, the students with average and lower academic credentials (undergraduate grades, English proficiency test scores, job, and research experiences) can hardly choose the universities that could match their profile. In this paper, we have analyzed some real unique data of Bangladeshi students who had been accepted admissions at different universities worldwide for higher studies. Finally, we have produced prediction models based on random forest (RF) and decision tree (DT) techniques, which can predict appropriate universities of specific classes for students according to their past academic performances. Two separate models have been studied in this paper, one for Masters (MS) students and another for Doctor of Philosophy (PhD) students. According to the Quacquarelli Symonds (QS) World University Rankings, the universities where the students got admitted have been divided into 9 classes for MS students and 8 classes for PhD students. Accuracy, precision, recall and F1-Score have been studied for the two machine learning algorithms. Numerical results show that both the algorithm DT and RF have the same accuracy of 89% for PhD student data and 86% for MS student data.
Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network Romi Fadillah Rahmat; Mhd Faris Pratama; Sarah Purnamawati; Sharfina Faza; Arif Ridho Lubis; Al-Khowarizmi Al-Khowarizmi; Muharman Lubis
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Glioma as one of the most common types of brain tumor in the world has three different classes based on its cell types. They are astrocytoma, ependymoma, oligodendroglioma, each has different characteristics depending on the location and malignance level. Radiological examination by medical personnel is still carried out manually using magnetic resonance imaging (MRI) medical imaging. Brain structure, size, and various forms of tumors increase the level of difficulty in classifying gliomas. It is advisable to apply a method that can conduct gliomas classification through medical images. The proposed methods were proposed for this study using deep convolutional neural network (DCNN) for classification with k-means segmentation and contrast enhancement. The results show the effectiveness of the proposed methods with an accuracy of 95.5%.
A survey and analysis of intrusion detection models based on information security and object technology - cloud intrusion dataset (ISOT-CID) Yassine Ayachi; Youssef Mellah; Mohammed Saber; Noureddine Rahmoun; Imane Kerrakchou; Toumi Bouchentouf
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Nowadays society, economy, and critical infrastructures have become principally dependent on computers, networks, and information technology solutions, on the other side, cyber-attacks are becoming more sophisticated and thus presenting increasing challenges in accurately detecting intrusions. Failure to prevent intrusions could compromise data integrity, confidentiality, and availability. Different detection methods are proposed to tackle computer security threats, which can be broadly classified into anomaly-based intrusion detection systems (AIDS) and signature-based intrusion detection systems (SIDS). One of the most preferred AIDS mechanisms is the machine learning-based approach which provides the most relevant results ever, but it still suffers from disadvantages like unrepresentative dataset, indeed, most of them were collected during a limited period of time, in some specific networks and mostly don't contain up-to-date data. Additionally, they are imbalanced and do not hold sufficient data for all types of attacks, especially new attack types. For this reason, up-to-date datasets such as information security and object technology-cloud intrusion dataset (ISOT-CID) are very convenient to train predictive models on a cloud-based intrusion detection approach. The dataset has been collected over a sufficiently long period and involves several hours of attack data, culminating into a few terabytes. It is large and diverse enough to accommodate machine-learning studies. 

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