Articles
59 Documents
<![CDATA[Survey on Early Detection of Alzhiemer’s Disease Using Capsule Neural Network ]]>
<![CDATA[Sharunya Sharunya R]]>;
<![CDATA[Vijayalakshmi Desai]]>;
<![CDATA[Meenakshi Singh]]>;
<![CDATA[Kusuma Mohanchandra]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 7 No 1 (2020) ]]>
Publisher : <![CDATA[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-0701.65
<![CDATA[Alzheimer's disease (AD) is an disorder which is irreversible of the brain related to memory loss, mostly found in the old and aged population. Alzheimer's dementia results from the degeneration or loss of brain cells. The brain-imaging technologies most often used to diagnose AD is Magnetic resonance imaging (MRI). MRI or structural magnetic resonance is a very popular and actual technique used to diagnose AD. An MRI uses magnets and powerful radio waves to create a complete view of your brain. To actually detect the presence of Alzheimer’s, the MRI should me studied carefullyImplementation of CBIR Content Based Image Retrival which is a revolutionary computer aided diagnosis technique will create new abilities in MRI Magnetic resonance imaging in related image retrieval and training for recognition of development of AD in early stages]]>
<![CDATA[Development of Human-Computer Interactive Interface for Intelligent Automotive ]]>
<![CDATA[Lyu Jianan]]>;
<![CDATA[Ashardi Abas]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 7 No 2 (2020) ]]>
Publisher : <![CDATA[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-0702.134
<![CDATA[The wide application of information technology and network technology in automobiles has made great changes in the Human-computer interaction. This paper studies the influence of Human-computer interaction modes on driving safety, comfort and efficiency based on physical interaction, touch screen control interaction, augmented reality, speech interaction and somatosensory interaction. The future Human-com-puter interaction modes such as multi-channel Human-computer interaction mode and Human-computer interaction mode based on biometrics and perception techno-logy are also discussed. At last, the method of automobile Human-computer interaction design based on the existing technology is proposed, which has certain guiding significance for the current automobile Human-computer interaction interface design.]]>
<![CDATA[Teaching Programming Using the Robot-Based Learning Approach ]]>
<![CDATA[Stephanus Mberema Kangungu]]>;
<![CDATA[Maizatul Hayati Mohamad Yatim]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 7 No 2 (2020) ]]>
Publisher : <![CDATA[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-0702.145
<![CDATA[This paper discusses learning theories, STEM, educational robotics as well as the current generation of students found in classrooms by reviewing previous and current academic literature on these topics, to assist in ascertaining the current advancements and theories related to the Robot-based learning approach as well as how these advancements have helped improve this approach and enabled educators to better make use of it. Furthermore, this paper reviews previous academic literature on computer programming to discuss the current learning approaches in use and the kind of learning tools being utilized. Once this topics are reviewed the reader can have a clear picture of the learning approach, what learning theory does it belong too, the type of students found in the classroom as well as what motivates them and the subject that is being taught as well as the different learning tools for this subject. The reader will also learn why improving the effectiveness of how programming is learned helps create more students good in STEM and how it assists in realising the Malaysian Educational Development Plan. This paper reviewed literature from the year 2014 and above as the information is more relevant and current, except for literature that is from a leading or renowned individual in any field that is being discussed in this dissertation.]]>
<![CDATA[Preliminary on Human Driver Behavior: A Review ]]>
<![CDATA[Xiao Yan]]>;
<![CDATA[Ashardi Abas]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 7 No 2 (2020) ]]>
Publisher : <![CDATA[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-0702.146
<![CDATA[Drowsiness is one of the main factors causing traffic accidents. Research on drowsiness can effectively reduce the traffic accident rate. According to the existing literature, this paper divides the current measurement techniques into subjective and objective ones. Among them, invasive detection and non-invasive detection based on vehicles or drivers are the main objective detection methods.Then, this paper studies the characteristics of drowsiness, and analyzes the advantages and disadvantages of each detection method in practical application. Finally, the development of detection technology is prospected, and provides ideas for the follow-up development of fatigue driving detection technology.]]>
<![CDATA[Indonesia Network Infrastructures and Workforce Adequacy to Implement Machine Learning for Large-Scale Manufacturing ]]>
<![CDATA[Anderson, Steven]]>;
<![CDATA[Lawi, Ansarullah]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 8 No 1 (2021) ]]>
Publisher : <![CDATA[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
<![CDATA[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.]]>
<![CDATA[A Comprehensive Review on Artificial Intelligence Techniques for Covid-19 Pandemic ]]>
<![CDATA[Anisha]]>;
<![CDATA[Saranya]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 8 No 1 (2021) ]]>
Publisher : <![CDATA[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
<![CDATA[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.]]>
<![CDATA[Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture ]]>
<![CDATA[Kamath, Deepthi]]>;
<![CDATA[Fathima, Misba Firdose]]>;
<![CDATA[K. P., Monica]]>;
<![CDATA[Kusuma, M.]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 8 No 1 (2021) ]]>
Publisher : <![CDATA[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
<![CDATA[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.]]>
<![CDATA[Alzheimer's Disease: A Survey ]]>
<![CDATA[Harshitha]]>;
<![CDATA[Chamarajan, Gowthami]]>;
<![CDATA[Y, Charishma]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 8 No 1 (2021) ]]>
Publisher : <![CDATA[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
<![CDATA[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.]]>
<![CDATA[Brain Computer Interface for Emergency Virtual Voice ]]>
<![CDATA[Arpitha]]>;
<![CDATA[Binduja]]>;
<![CDATA[Jahnavi]]>;
<![CDATA[Mohanchandra, Kusuma]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 8 No 1 (2021) ]]>
Publisher : <![CDATA[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
<![CDATA[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.]]>
<![CDATA[Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture ]]>
<![CDATA[Kamath, Deepthi]]>;
<![CDATA[Fathima, Misba Firdose]]>;
<![CDATA[K. P, Monica]]>;
<![CDATA[Mohanchandra, Kusuma]]>
<![CDATA[ International Journal of Artificial Intelligence Vol 8 No 2: December 2021 ]]>
Publisher : <![CDATA[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-0802.232
<![CDATA[Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.]]>
