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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 5 Documents
 1 
Search results for , issue "Vol 8 No 1 (2021)" : 5 Documents clear
Indonesia Network Infrastructures and Workforce Adequacy to Implement Machine Learning for Large-Scale Manufacturing Anderson, Steven; Lawi, Ansarullah
International Journal of Artificial Intelligence Vol 8 No 1 (2021)
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-0801.182

Abstract

Technological development prior to industrial revolution 4.0 incentivized manufacturing industries to invest into digital industry with the aim of increasing the capability and efficiency in manufacturing activity. Major manufacturing industry has begun implementing cyber-physical system in industrial monitoring and control. The system itself will generate large volumes of data. The ability to process those big data requires algorithm called machine learning because of its ability to read patterns of big data for producing useful information. This study conducted on premises of Indonesia’s current network infrastructure and workforce capability on supporting the implementation of machine learning especially in large-scale manufacture. That will be compared with countries that have a positive stance in implementing machine learning in manufacturing. The conclusions that can be drawn from this research are Indonesia current infrastructure and workforce is still unable to fully support the implementation of machine learning technology in manufacturing industry and improvements are needed.
A Comprehensive Review on Artificial Intelligence Techniques for Covid-19 Pandemic Anisha; Saranya
International Journal of Artificial Intelligence Vol 8 No 1 (2021)
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-0801.193

Abstract

The pandemic situation due to the emergence of Covid-19 presents various problems physically, economically and mentally for the individuals world-wide, therefore faster solutions with wider access is essential to solve the problems which aids as a support to the healthcare. This is made possible through the incorporation of Artificial Intelligence (AI) technology to handle the situation of pandemic. This paper aims to present a comprehensive re-view of the applications employed using AI for the problems faced during Covid-19 pandemic. The AI applications involved in screening, predicting, forecasting, neighborhood contact tracing and drug discovery of Covid-19 are addressed in this review. This review also presents detailed working of AI algorithms in each application. This paper helps the researchers with vivid information of AI applications of Covid-19 pandemic.
Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture Kamath, Deepthi; Fathima, Misba Firdose; K. P., Monica; Kusuma, M.
International Journal of Artificial Intelligence Vol 8 No 1 (2021)
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-0801.217

Abstract

Alzheimer’s disease is a condition that leads to, progressive neurological brain disorder and destroys cells of the brain thereby causing an individual to lose their ability to continue daily activities and also hampers their mentality. Diagnostic symptoms are experienced by patients usually at later stages after irreversible neural damage occurs. Detection of AD is challenging because sometimes the signs that distinguish AD MRI data, can be found in MRI data of normal healthy brains of older people. Even though this disease is not completely curable, earlier detection can aid in promising treatment and prevent permanent damage to brain tissues. Age and genetics are the greatest risk factors for this disease. This paper presents the latest reports on AD detection based on different types of Neural Network Architectures.
Alzheimer's Disease: A Survey Harshitha; Chamarajan, Gowthami; Y, Charishma
International Journal of Artificial Intelligence Vol 8 No 1 (2021)
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-0801.220

Abstract

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease.
Brain Computer Interface for Emergency Virtual Voice Arpitha; Binduja; Jahnavi; Mohanchandra, Kusuma
International Journal of Artificial Intelligence Vol 8 No 1 (2021)
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-0801.222

Abstract

Brain computer interface (BCI) is one of the thriving emergent technology which acts as an interface between a brain and an external device. BCI for speech communication is acquiring recognition in various fields. Speech is one of the most natural ways to express thoughts and feelings by articulate vocal sounds. The purpose of this study is to restore communication ability of the people suffering from severe muscular disorders like amyotrophic lateral sclerosis (ALS), stroke which causes paralysis, locked-in syndrome, tetraplegia and Myasthenia gravis. They cannot interact with their environment even though their intellectual capabilities are intact. Our work attempts to provide summary of the research articles being published in reputed journals which lead to the investigation of published BCI articles, BCI prototypes, Bio-Signals for BCI, intent of the articles, target applications, classification techniques, algorithms and methodologies, BCI system types. Thus, the result of detailed survey presents an outline of available studies, recent results and looks forward to future developments which provides a communication pathway for paralyzed patients to convey their needs.

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