An improved unsupervised mapping technique using AMSOM for neurodegenerative disease detection

Suwalka, Isha; Agrawal, Navneet

doi:10.1504/ijcsyse.2018.091402

Cited by 4 publications

(1 citation statement)

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“…Varian algoritme ini juga memungkinkan dilakukannya penambahan dan penghapusan neuron (menyesuaikan terhadap data set) sehingga diharapkan dapat merepresentasikan data set dengan lebih efisien. Algoritme AMSOM telah digunakan oleh Suwalka (Suwalka & Agrawal, 2018) (Suwalka & Agrawal, 2019) dalam pencitraan medis untuk melakukan deteksi penyakit neurodegenerative dan identifikasi tumor. Hasil dari penelitian menunjukkan AMSOM memberikan hasil yang cukup baik dan dapat mencapai target yang diharapkan.…”

Section: Pendahuluanunclassified

Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps

Prasetyo

Supianto

Anam

et al. 2022

JTIIK

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Digitalisasi proses pembelajaran memungkinkan untuk dihasilkannya rekaman terhadap setiap aktivitas siswa selama belajar. Rekaman yang dihasilkan tersebut dapat digunakan untuk mengelompokkan siswa berdasarkan pola dari proses belajar yang dilakukan. Hasil pengelompokkan yang peroleh dapat digunakan untuk melakukan penyesuaian komponen pembelajaran ataupun metode pembelajaran bagi siswa. Salah satu metode pengelompokan yang sering digunakan adalah Self-Organizing Maps (SOM), SOM merupakan metode jaringan syaraf tiruan dengan tujuan untuk mempertahankan topologi data ketika data input multidimensi diubah menjadi data output dengan dimensi yang lebih rendah. Neuron SOM pada dimensi input diperbaharui sepanjang proses pelatihan, sedangkan neuron pada dimensi output tidak mendapatkan pembaruan sama sekali, hal ini menyebabkan struktur neuron yang digunakan pada tahapan inisialisasi akan tetap sama hingga akhir proses pengelompokan. Pada penelitian ini menggunakan metode Adaptive Moving Self-Organizing Maps (AMSOM) yang menggunakan struktur neuron lebih fleksibel, dengan dimungkinkannya terjadi perpindahan, penambahan dan penghapusan dari neuron menggunakan data 12 assignments dari media pembelajaran MONSAKUN. Hasil penelitian menunjukkan terdapat perbedaan yang signifikan secara statistik antara nilai quantization error dan nilai topographic error dari algoritme AMSOM dengan algoritme SOM. Metode AMSOM menghasilkan rata-rata nilai quantization error 27 kali lebih kecil dan rata-rata nilai topographic error 54 kali lebih kecil dibandingkan dengan metode SOM.AbstractThe digitization of the learning process makes it possible to produce recordings of each student's activity during learning. The resulting record can be used to group students based on the pattern of the learning process. The grouping results can be used to make adjustments to the learning components or learning methods for students. One of the most frequently used clustering methods is Self-Organizing Maps (SOM), SOM is a neural network method to maintain data topology when multidimensional input data is converted into output data with lower dimensions. The SOM neurons in the input dimension are updated throughout the training process, while the neurons in the output dimension do not get updated at all, this causes the neuron structure used in the initialization stage to remain the same until the end of the grouping process. In this study, the Adaptive Moving Self-Organizing Maps (AMSOM) method uses a more flexible neuron structure, allowing for the transfer, addition and deletion of neurons using 12 assignments of data from MONSAKUN learning media. The results showed that there was a statistically significant difference between the quantization error and the topographic error of the AMSOM algorithm and the SOM algorithm. The AMSOM method produces an average quantization error 27 times smaller and an average topographic error 54 times smaller than the SOM method.

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Section: Pendahuluanunclassified

Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps

Prasetyo

Supianto

Anam

et al. 2022

JTIIK

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A Review on Land-use and Land-change with Machine Learning Algorithm

Gupta

Sethi

Bathija

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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A significant proportion of the planet’s ground environment has changed with ground use and land-related shifts, exacerbated by both human behavior and natural feedback. Anthropogenic activities have dramatically altered natural ecosystems, in particular in areas greatly affected by population growth and climate change such as Eastern Africa. For environmental protection and successful water management practices, it is important to be aware of the trends in land use & land cover (LULC). This study centered on developments in LULC patterns in Climate Modelling, remote sensing, and field data combined to identify positive feedback circuits or negative ones using remote sensing techniques and geographic information systems (GIS). Furthermore, its findings include statements that focus on policies that display the impacts and levels of LULC transformation and the dissemination of these improvements in time and space as a central element in the current methods for environmental control of changes and the management of natural resources.

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Machine learning using AMKSOM for Identifying Tumor around Edema region

Suwalka

Agrawal

2019

2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP)

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An improved unsupervised mapping technique using AMSOM for neurodegenerative disease detection

Cited by 4 publications

References 0 publications

Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps

Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps

A Review on Land-use and Land-change with Machine Learning Algorithm

Machine learning using AMKSOM for Identifying Tumor around Edema region

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