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International Journal of Artificial Intelligence
Published by Lamintang Education & Training Centre
ISSN : 24077275     EISSN : 26863251     DOI : https://doi.org/10.36079/lamintang.ijai
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
The aim is to publish high-quality articles dedicated to Artificial Intelligence. IJAI published in biannual, and in Indonesian, Malay and English.
Arjuna Subject : -
Articles 59 Documents
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Mobile Disinfectant Spraying Robot and its Implementation Components for Virus Outbreak: Case Study of COVID-19 Udoka, Eze Val Hyginus; Edozie, Enerst; Davis, Musika; Dickens, Twijuke; Janat, Wantimba; Wisdom, Okafor; Umaru, Kalyankolo; Nafuna, Ritah; Yudaya, Nansukusa
International Journal of Artificial Intelligence Vol 10 No 2: December 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01002.551

Abstract

The virus pandemic COVID-19 outbreak brought a huge pressure to the public healthcare system worldwide, especially in developing African countries like Uganda. The Educational system and institutions were put on a standstill due to no quick countermeasures to make the environment clean and safe for normal activities to continue. This paper successfully and comprehensively reviewed the Bluetooth and smart disinfectant spraying robot that successfully controlled the spread of the deadly virus. It also detailed different components that made up the complete spraying robot systems and from this it was observed that spraying robot systems are made up of almost the same components for implementations but differs on program that is embedded on the microcontroller due to different functions. This programing differs based on the functions that the designer/programmer wants the robot to do despite using almost the same components. This research review paper will act as guide for future researchers when designing and implementing a mobile spraying robot.
Point of Sale System Using Convolutional Neural Network for Image Recognition in Grocery Store Roslan, Naim Najmi; Saad, Ahmad Fadli
International Journal of Artificial Intelligence Vol 10 No 2: December 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01002.553

Abstract

The history of point of sale already has been told from a long time ago. The business nowadays is opting for the point-of-sale transactions because it was easy to sell the item to people face to face. This will build some trust between the cashier and the customer. The popular store that always customer need was the grocery store. However, the grocery store nowadays still not has a good feature for the point-of-sale system. The cashier still needs to scan the item through barcode scanner. This idea was led to make the point-of-sale transactions easier in the grocery store by applying the machine learning to the system. The problem for this project is the customer wait for a long time for their point-of-sale transactions to finish when bought the grocery items. The aim of this project is to detect the grocery items with convolutional neural network model for image recognition through camera within the main user interface. The Agile Development Life Cycle (ADLC) method is used in the development of Point-of-Sale System using Machine Learning for Image Recognition in Grocery Store. Moreover, this project is to evaluate the usability of the system using Post-Study System Usability Questionnaire (PSSUQ) approach. The PSSUQ evaluation is evaluated by the users of the system. The results of PSSUQ stated that the users satisfied with the system. The future research for this project is to make the point-of-sale system with a better model in the future. In conclusion, the system is works well and machine learning image recognition model also can detect the grocery item clearly.
A Deep Reinforcement Learning Agent for Snake Game Hossain, Md Meem; Fakokunde, Akinwumi; Olaolu, Omololu Isaac
International Journal of Artificial Intelligence Vol 10 No 2: December 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01002.565

Abstract

After watching AlphaGo a Netflix documentary which presents how AlphaGo is an AI computer game developed by deep-mind technologies based on deep reinforcement learning (DRL). Since then, my interest in reinforcement learning has been growing. In this project, I will apply reinforcement learning to develop an agent to play snake game. Where Deep learning will implement a neural Network to help the agent (snake) to learn what action must take to get a state. If we describe deep reinforcement learning (DRL) model where agent interacts with an environment and chooses an action. Based on action, agents receive feedback from the environment as states (or perceives) and rewards. A state = an array with 11 input values, each input values represent a neural network that provides an output of 3 values, each one represents three possible actions the agent (snake) can take (Straight, Right Turn and Left Turn).
A Review of Cross-Platform Document File Reader Using Speech Synthesis Chukwudi, Ogenyi Fabian; Eze, Val Hyginus Udoka; Chinyere, Ugwu
International Journal of Artificial Intelligence Vol 10 No 2: December 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01002.569

Abstract

Document files are files used to store documents on storage devices primarily for computer use. Software is used to view these files, displaying their text content in a legible way. However, it is essential to have programs for transforming electronic files into versions usable by those who suffer from specific disabilities. This paper reviewed fifteen published articles in the field of document file reading. It was observed from the review that various attempts have been made by different researchers in order to develop a software cable for converting document files that consist of text to an audio format. Text may now be easily translated into natural-sounding voice across many platforms using different software. It was observed from the systematic review that the use of AI such as the GPT-3.5 and GPT-4 Turbo Large Language Model (LLM) technologies has the best performance because it does not end at producing a vocal sound that is similar to human own, but it also translates different languages. In conclusion, cross-platform document file reader (text-to-speech) synthesis has improved user experiences in a variety of applications such as language learning, audiobooks and virtual assistants.
Enhancing Hydropower Management through Artificial Intelligence: Insights from Norway's Experience Jeroen, Claude; Pettersen, Juzeniene; Hyysalo, Kjesbu
International Journal of Artificial Intelligence Vol 11 No 1: June 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01101.218

Abstract

Norway is a global leader in renewable energy, with hydropower accounting for 90% of its electricity generation. The country's hydropower sector is crucial to both national and international energy demands, and the need for efficient management has become more pressing as the world shifts from fossil fuels to cleaner energy sources. Artificial Intelligence (AI) is emerging as a powerful tool for optimizing hydropower management by improving predictive analytics, automating decision-making, and processing real-time data. In Norway, AI is increasingly being used to forecast water flow and manage energy production more effectively, while also enhancing predictive maintenance to minimize downtime and operational costs. Despite its potential, the implementation of AI faces challenges such as high costs, infrastructure investments, and data privacy concerns. This article explores recent innovations in AI applied to hydropower in Norway, discussing both the opportunities and challenges. The successful integration of AI into hydropower operations holds promise for improving efficiency and sustainability, offering insights for broader adoption across the global renewable energy sector. Future developments in AI and its application in renewable energy, such as smart grids and interconnecting different energy sources, could further enhance the energy landscape.
EEG-based Classifications of Alzheimer’s Disease by Using Machine Learning Techniques C. R, Nagarathna
International Journal of Artificial Intelligence Vol 11 No 1: June 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01101.601

Abstract

The study has shown how classifiers behave when identifying and categorizing Alzheimer's disease stages. The main characteristics of various frequency bands were fed into the classifier as input. The accuracy of recognition is evaluated using machine learning classifiers. The effort aims to create a novel model that combines "preprocessing, feature extraction, and classification" to identify different stages of disease. The study starts with bands filtering, moves on to feature extraction, which derives several bands from the EEG signals, and then employs KNN, SVM, and MLP algorithms to measure classification performance. "AD detection and classification using machine learning classifiers KNN, SVM, and MLP" is the main focus of this research. Five wavelet band characteristics are used by the built classifiers to categorize different illness phases. These characteristics are computed using DWT, PCA, and ICA, which aid in obtaining wavelet-related knowledge for learning. The proposed machine learning model achieves a classification accuracy of 95% overall.
Drone Based Fire Detection System Based on Convolutional Neural Network Rahman, Hanif Ikmal; Saad, Ahmad Fadli; Yani, Achmad
International Journal of Artificial Intelligence Vol 11 No 1: June 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01101.669

Abstract

Open fires are happening more and more throughout Malaysia. It is either intentional or accidental fire. The most dangerous is an accidental fire because it may not be detected by anyone until it becomes large. Detecting a fire is not an easy task. People may not see an ongoing fire because it may be too far away, or the fire may be too small. The objective of this project is to build a fire detection system. Fire detecting systems are developed to ensure more accurate fire detection. To ensure accurate fire detection, this project uses a waterfall methodology. This project uses drones as a tool to help with fire detection. Using a Convolutional Neural Network (CNN), this project implements the use of the PyTorch framework in detecting fires. The testing was done with a distance of 2 meters from the fire and a height of 2 meter from the ground. Edited images were used and uploaded to the system. Accuracy results of 80% can ensure accurate fire detection. To evaluate the system, edited fire images are used to ensure the accuracy of the system. Therefore, CNN is a good tool for detecting fires.
The Role of Artificial Intelligence in Disaster Prediction, Mitigation, and Response in the Philippines: Challenges and Opportunities Baltazar, Rommel; Florencio, Bacabac; Vicente, Aguda; Belizario, Phillip
International Journal of Artificial Intelligence Vol 11 No 1: June 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01101.675

Abstract

One of the most disaster-prone countries globally, experiences frequent natural calamities, including typhoons, earthquakes, and floods is the Philippines. This study explores the role of AI in enhancing disaster prediction, risk management, and mitigation in the Philippines. Using a qualitative research approach, semi-structured interviews were conducted between June 2023 and March 2024 with key stakeholders, including disaster management officials, meteorologists, and researchers. The findings highlight how AI technologies, particularly machine learning and neural networks, have significantly improved disaster forecasts by processing extensive datasets from meteorological, seismic, and geographical sources. AI-driven models are enhancing the accuracy of predictions for typhoons, earthquakes, and flood risks, contributing to more effective early warning systems and timely evacuation protocols. Despite these advancements, challenges remain, including limitations in infrastructure, budget constraints, and data quality, which hinder the full adoption of AI in disaster risk management (DRM). Nevertheless, the study identifies substantial opportunities for further development, emphasizing international collaboration and policy support to promote AI integration in DRM. The findings suggest that AI holds immense potential to revolutionize disaster response strategies in the Philippines, and further research is needed to address technical barriers and enhance AI’s role in building resilient communities.
An Innovative Technique for Medical Image Segmentation Using Convolutional Neural Networks Optimized Through Stochastic Gradient Descent Taheri, Mohammad; Sadeghi, Faezeh; Koochari, Abbas
International Journal of Artificial Intelligence Vol 11 No 1: June 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01101.688

Abstract

Medical image segmentation is crucial due to its essential role in disease therapy. Various challenges such as hair artifacts, illumination variations, and different imaging acquisitions complicate this task. In this paper, we introduce a novel convolutional neural network (CNN) architecture designed to address these challenges. We also compare our method with two well-known architectures, Unet and FCN, to demonstrate the superiority of our approach. Our results, evaluated using four metrics, accuracy, dice coefficient, Jaccard index, and sensitivity show that our method outperforms the other two. We employed Jaccard distance and binary cross-entropy as the loss functions and used SGD+Nesterov as the optimization algorithm, which proved more effective than the Adam optimizer. In the preprocessing step, we included image resizing to speed up the process and image augmentation to enhance the evaluation metrics. As a postprocessing step, we applied a threshold technique to the algorithm's outputs to increase the contrast of the final images. This method was tested on two well-known and publicly available medical image datasets: PH2 for melanoma detection and Chest X-ray images for detecting chest lesions or COVID-19.
Ensemble Stacking Method of Classifying the Stages of Alzheimer's Disease by using MRI Dataset Nagarathna; Kusuma; Huliyappa, Harsha
International Journal of Artificial Intelligence Vol 11 No 2: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01102.602

Abstract

Alzheimer's Disease (AD) is a progressive neurological disorder that gradually impairs an individual's memory, reasoning, and ability to perform daily tasks. Early and accurate diagnosis of AD is essential for effective intervention, yet remains challenging due to the complexity of its progression. This study explores the use of an ensemble stacking approach to evaluate the effectiveness of transfer learning techniques in classifying various stages of Alzheimer's disease. Unlike traditional methods that directly analyze raw brain images, this research implements a preprocessing technique using the Markov Random Field method to extract the brain tissues specifically affected by AD. These segmented brain tissues are then utilized to train base models, consisting of three convolutional neural networks (CNNs) with varying configurations. The predictions of these base models are ensembled and further refined through a second-level meta-model to enhance classification accuracy. The proposed ensemble stacking framework was evaluated using an MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which contains images categorized into Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and Healthy Control (HC) groups. The meta-model demonstrated superior performance, achieving an average accuracy of 97%, along with high precision, recall, and F1 scores. This study highlights the potential of ensemble learning and transfer learning in advancing AD diagnosis, offering a robust and efficient approach for categorizing its various stages based on medical imaging data.

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