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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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Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization Fariza Abu Samah, Khyrina Airin; Athirah Ahmad, Nurul; Amilah Shari, Anis; Fakhira Almarzuki, Hana; Arafah, Zuhri; Septem Riza, Lala; Abdul Halim, Amir Haikal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4334-4343

Abstract

In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources Shankara, Kavitha Hosakote; Hosakote Shankara, Kavitha; Srikantaswamy, Mallikarjunaswamy; Nagaraju, Sharmila
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp307-316

Abstract

This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management.
Enhancing video anomaly detection for human suspicious behavior through deep hybrid temporal spatial network Sriram, Kusuma; Purushotham, Kiran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4121-4128

Abstract

Abnormal behavior exhibited by individuals with particular intentions is common, and when such behavior occurs in public places, it can cause physical and mental harm to others. Considering the rise in the automated approach for anomaly detection in videos, accuracy becomes essential. Most existing models follow a deep learning architecture, which faces challenges due to variations in motion. This research work develops a deep learning based hybrid architecture with temporal and spatial features. The hybrid temporal spatial network (HTSNet) consists of two customized architectures: a graph neural network (GNN) and a convolutional neural network (CNN). HTSNet combined with a novel classifier to extract features and classify normal and abnormal behavior. The performance of HTSNet is rigorously evaluated using the University of California, San Diego-Pedestrian 1 (UCSD Ped1) dataset, a benchmark in computer vision research for anomaly detection in video surveillance. The effectiveness of HTSNet is demonstrated through a comparative analysis with current state-of-the-art methods, using the area under the curve (AUC) metric as a standard measure of performance. This paper contributes to the advancement of video surveillance technology, providing a robust framework for enhancing public safety and security in an increasingly digital world.
Unmanned aircraft vehicles/unmanned aerial systems digital twinning: Data-driven lift and drag prediction for airfoil design Aghazadeh Ardebili, Ali; Martella, Angelo; Khalil, Adem; Khalil, Sabri; Longo, Antonella; Ficarella, Antonio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp240-251

Abstract

This study investigates the innovative application of neural networks algorithms in the aviation industry's mechanical design process, motivated by the pursuit of creating a more accurate and efficient method for performance prediction. Traditional approaches, such as computational fluid dynamics (CFD) simulations based on solving Navier-Stokes’s equations, demand substantial computational power and often exhibit limited accuracy, particularly when compared with complex geometries. The state-of-the-art review unveils a growing research trend advocating for data-driven methodologies to revolutionize design practices, addressing the limitations of conventional techniques. The primary objective of this study is to explore how neural network algorithms can overcome the drawbacks of CFD simulations, offering a more effective alternative for predicting the performance of airfoils. To achieve this objective, we conducted a performance analysis of airfoils using neural network algorithms. The results presented a promising avenue for a more accurate and efficient performance prediction method through digital twinning. The study highlights the advantageous features of neural network methods in unmanned aircraft vehicles (UAV) component mechanical design, showcasing their potential to outperform traditional methods and offering practical recommendations for integration into the design process.
Spectral splitting of speech signal using time varying recursive filters for binaural hearing aids Chilakawad, Aparna; N. Kulkarni, Pandurangarao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4998-5004

Abstract

Speech perception in noisy environments is reduced in human with sensorineural hearing loss (SNHL) due to masking. Moderate SNHL cannot be cured medically hence masking effects should be reduced to enhance speech perception. To reduce masking, processing delay and hardware complexity the present paper is proposed a scheme to partition the voice signal into two signals which are complementary to each other by using the filter-bank summation method (FBSM) with a set of time-varying recursive band pass filters. Performance of the filter is evaluated with following measures: perceptual evaluation of speech quality (PESQ), mean opinion score (MOS) for speech quality and modified rhyme test (MRT) for speech intelligibility. The test signals used for the evaluation of quality are a syllable and a word and for the evaluation of intelligibility 300 monosyllabic words are used. The results demonstrated an increase in the quality and intelligibility of processed speech in a noisy environment. As a result, there is an enhancement in perception of processed speech in a noisy environment.
Predicting water resistance and pitching angle during take-off: an artificial neural network approach Fajar, Muhammad; Atmaja, Sigit Tri; Pinindriya, Sinung Tirtha; Soemaryanto, Arifin Rasyadi; Hidayat, Kurnia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp142-150

Abstract

This research addresses the challenges faced by seaplanes and amphibious aircraft during takeoff and landing on water, emphasizing the limitations and costs associated with traditional towing tank tests and computational fluid dynamics (CFD) simulations. The study proposes an innovative approach that employs artificial neural networks (ANN) to predict water resistance and pitching angle during amphibious aircraft take-off, minimizing the reliance on expensive towing tank tests. The ANN models are developed and optimized using Bayesian optimization, showcasing improved accuracy in predicting water resistance and pitching angle. The research demonstrates the potential of machine learning, specifically ANNs, to significantly reduce the need for costly experimental tests, providing an efficient alternative for designing amphibious aircraft. The results indicate high accuracy in predicting water resistance and pitching angle, offering substantial time and resource savings during the experimental phase. However, the study highlights the need for model adaptation for different designs and test variations to enhance overall applicability.
Malay phoneme-based subword news headline generator for low-resource language Tsann Phua, Yeong; Hooi Yew, Kwang; Fadzil Hassan, Mohd; Yok Wooi, Matthew Teow
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4965-4975

Abstract

The booming of technology has significantly increased the amount of news articles for readers. The headline of news plays an essential role in attracting readers. Traditionally, crafting the news headline is a manual task at the news desk. The motivation of this paper is to address the issues faced in low resource languages, such as the Malay language. The main contribution of this paper is a new hybrid model based on extractive- and abstractive-based text summarization with the integration of a geographical linguistics model; a Malay phoneme-based subword embedding has been developed to solve the complex morphological issue in the Malay language-based computational linguistic applications. The experiment involves various sequence-to sequence (seq2seq) models to generate the Malay news headlines. Besides that, the out-of-vocabulary (OOV) is assessed in the models. From the experiment, the proposed hybrid text summarization model shows significant improvement over the baseline models above 11.00 in ROUGE-1, 4.00 ROUGE-2, and 11.00 in ROUGE-L. The proposed model can reduce the OOV rate to below 15%.
Review of cloud computing models in education and the unmet needs Rezqallah Malkawi, Aminah; Abu Bakar, Muhamad Shahbani; Dahalin, Zulkhairi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4029-4036

Abstract

This article thoroughly examines recently proposed cloud computing (CC) models used within the higher educational institutions (HEI) field, scrutinizing their objectives, structures, and incorporated requirements. Each model's unique architecture and functionality are analyzed to understand their potential educational contributions. Beyond technical considerations, the study explores nuanced requirements essential for successful integration in educational settings. The review exposes diverse aims pursued by the models, such as enhanced scalability, collaborative learning, and resource management, emphasizing their capacity to reshape traditional educational paradigms. However, a notable gap emerges-the absence of cultural and requirement elicitation models within the frameworks. Despite growing cultural diversity and varied educational needs, most models lack components addressing cultural nuances and robust requirement elicitation. In conclusion, the paper identifies a pressing need for a transformative shift in developing CC models for education. The absence of dedicated cultural and requirement elicitation models challenges the holistic effectiveness of these frameworks. Future efforts should prioritize integrating culturally sensitive components and comprehensive requirement elicitation strategies to create adaptive, universally applicable, and inclusive CC educational environments. Addressing these gaps will pave the way for a nuanced and responsive integration of CC technologies in diverse educational settings.
A revolutionary convolutional neural network architecture for more accurate lung cancer classification Muliadi, Muliadi; Windarto, Agus Perdana; Solikhun, Solikhun; Alkhairi, Putrama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp516-526

Abstract

This research aimed to investigate a breakthrough in convolutional neural network (CNN) architecture with the potential to revolutionize lung cancer classification. The proposed method is a comparative optimization model of ResNet architecture, with accuracy rate of 99.68% in identifying and categorizing lung cancer types. The results showed that the use of innovative methods in CNN architecture, such as multi-dimensional convolutional layers and the integration of specific lung cancer features, effectively provided highly accurate and reliable outcomes. These showed a positive impact on the development of medical diagnostic technology, offering promising potential to enhance prognosis and response to treatment for lung cancer patients. With high accuracy rate, this breakthrough presents opportunities for further advancements in lung cancer management through artificial intelligence-based methods.
A detection model of aggressive driving behavior based on hybrid deep learning Khalid, Noor Walid; Abdullah, Wisam Dawood
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4883-4894

Abstract

Modern transportation faces a crucial challenge in ensuring road safety by addressing driving behavior concerns. This paper introduces an innovative deep learning model derived from a cellphone-collected Driving Behavior dataset, focusing on detecting and classifying aggressive driving. Using a cohort-based dataset, a hyper-deep learning model categorizes drivers into normal, slow, and aggressive groups. The system employs pre-processing methods and two methodologies, directly inputting data and incorporating feature selection. The hyper-CNN-Dense model, used for training, shows promising results. Feature selection techniques like SVD6 and MI6 achieve optimal outcomes, with a 100% accuracy rate in detecting aggressive driving. Notably, SVD6 boasts a short processing time of just 43 seconds. This research successfully identifies aggressive driving behavior with impeccable accuracy and in a remarkably short timeframe.

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