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Effective Alzheimer's Stage Classification Using Transfer Learning Based on MRI Images 7

Effective Alzheimer's Stage Classification Using Transfer Le...

引用

7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Hamdani, Hamdani Septiarini, Anindita Pawitra, Theresia Amelia Sumampouw, Aldion Tejawati, Andi Kurniawan, Tri Basuki Mulawarman University Dept. of Informatics Faculty of Engineering Samarinda Indonesia Mulawarman University Dept. of Industrial Engineering Faculty of Engineering Samarinda Indonesia Universitas Bina Darma Magister Information of Technology Postgraduate Program Palembang Indonesia

ISBN: (纸本)9798331505103

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most prevalent cause of dementia, significantly impacting cognitive functions and daily activities. Accurate and timely classification of AD stages is of paramount importance for the effective treatment and management of the disease. The aim of this research is to employ transfer learning in a convolutional neural network (CNN) to classify the stages of AD using magnetic resonance imaging (MRI) brain images, building on the aforementioned foundational principles. The CNN model employed in this study is MobileNetV3-Small, which has been trained on a comprehensive dataset of labeled MRI scans with the objective of accurately distinguishing the various stages of AD. The dataset, referred to as the 'Augmented Alzheimer MRI Dataset,' comprises 3 3, 9 8 4 augmented and 6, 4 0 0 original MRI images, which have been categorized into four stages: Very Mild Demented, Moderate Demented, Mild Demented, and NonDemented. The methodology comprises three principal stages: data preparation, model training, and model evaluation. A systematic assessment of various hyperparameters, including split ratio, learning rate, batch size, and dropout rate, was conducted to optimize model performance. The results demonstrate that fine-tuning the model significantly enhances classification accuracy, achieving a maximum accuracy of 9 6 % compared to 7 2 % without fine-tuning. A smaller learning rate was found to improve performance metrics by preventing the model from converging to local minima. These findings highlight the potential of transfer learning and hyperparameter optimization in developing robust models for the early detection and classification of AD, contributing to better patient outcomes and advancing the field of medical image analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Air Quality Prediction Using Machine Learning: Systematic Literature Review

Air Quality Prediction Using Machine Learning: Systematic Li...

引用

International Conference on informatics and Computing (ICIC)

作者： Muhamad Fuat Asnawi Nur Fitriyanto M. Agoeng Pamoengkas Ema Utami Hanif Al Fatta Student Informatics of Distance Learning Program Amikom University Yogyakarta Indonesia Magister of Informatics Engineering Amikom University Yogyakarta Indonesia Informatics of Distance Learning Program Amikom University Yogyakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Air pollution significantly impacts global health and the environment, causing millions of deaths annually. With rapid urbanization and industrialization, especially in countries like China, air quality has deteriorated due to pollutants such as PM2.5, PM10, $\mathrm{CO}_{2}, \mathrm{CO}, \mathrm{NO}_{2}$, and SO 2 . Traditional models often fail to predict air quality accurately due to their dependence on optimal parameter sets and data availability. Machine learning (ML) methods, however, offer superior performance by handling large datasets and complex variable relationships. This systematic literature review examines 77 articles from 2020 to 2024, highlighting the most effective ML methods for air quality prediction, including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and LightGBM. The review discusses the types of data used, evaluation metrics, and the models’ performance. The findings emphasize the potential of ML to improve air quality monitoring and prediction, contributing to better public health and environmental management.

关键词： Support vector machines Accuracy Atmospheric modeling Predictive models Air quality Data models Public healthcare Long short term memory Random forests Systematic literature review

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Effective Alzheimer’s Stage Classification Using Transfer Learning Based on MRI Images

Effective Alzheimer’s Stage Classification Using Transfer L...

引用

Vocational Education and Electrical engineering, International Conference on

作者： Hamdani Hamdani Anindita Septiarini Theresia Amelia Pawitra Aldion Sumampouw Andi Tejawati Tri Basuki Kurniawan Dept. of Informatics Faculty of Engineering Mulawarman University Samarinda Indonesia Dept. of Industrial Engineering Faculty of Engineering Mulawarman University Samarinda Indonesia Magister Information of Technology Postgraduate Program Universitas Bina Darma Palembang Indonesia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most prevalent cause of dementia, significantly impacting cognitive functions and daily activities. Accurate and timely classification of AD stages is of paramount importance for the effective treatment and management of the disease. The aim of this research is to employ transfer learning in a convolutional neural network (CNN) to classify the stages of AD using magnetic resonance imaging (MRI) brain images, building on the aforementioned foundational principles. The CNN model employed in this study is MobileNetV3-Small, which has been trained on a comprehensive dataset of labeled MRI scans with the objective of accurately distinguishing the various stages of AD. The dataset, referred to as the “Augmented Alzheimer MRI Dataset,” comprises $\mathbf{3 3, 9 8 4}$ augmented and $\mathbf{6, 4 0 0}$ original MRI images, which have been categorized into four stages: Very Mild Demented, Moderate Demented, Mild Demented, and NonDemented. The methodology comprises three principal stages: data preparation, model training, and model evaluation. A systematic assessment of various hyperparameters, including split ratio, learning rate, batch size, and dropout rate, was conducted to optimize model performance. The results demonstrate that fine-tuning the model significantly enhances classification accuracy, achieving a maximum accuracy of $\mathbf{9 6 \%}$ compared to $\mathbf{7 2 \%}$ without fine-tuning. A smaller learning rate was found to improve performance metrics by preventing the model from converging to local minima. These findings highlight the potential of transfer learning and hyperparameter optimization in developing robust models for the early detection and classification of AD, contributing to better patient outcomes and advancing the field of medical image analysis.

关键词： Training Analytical models Accuracy Systematics Magnetic resonance imaging Computational modeling Transfer learning Data models Convolutional neural networks Alzheimer's disease

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Entropy and information gain analysis on low cost BCI for motorbike users to prevent accident 4

Entropy and information gain analysis on low cost BCI for mo...

引用

4th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2019

作者： Awangga, Rolly Maulana Pane, Syafrial Fachri Majesty, Dinda Setiawan, Asep Asyhari, Moch Yusuf Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Universitas Islam Indonesia Magister Program of Informatics Engineering Yogyakarta Indonesia

ISBN: (纸本)9781728151182

The phenomenon of mothers giving the wrong vehicle signal lights to turn left and right reminds the effect of giving the right signal lights to other road users. Weak human concentration while driving can cause errors when giving vehicle signal lights or not giving vehicle signal lights. Mistakes when giving a signal light a vehicle has a risk of an accident. The driver's brain wave data will show the results of the rider's brain thinking when turning. This study aims to analyze the concentration of the driver when driving. This research will obtain driver brain wave data when driving and analyze human brain waves while riding a motorcycle, especially when turning left and right, to reduce the risk of accidents while driving. The driver will use brain waves tool while driving, and the computer will detect the driver's brain waves, and then send brainwave data to the computer. Driver brain waves are analyzed using the formula entropy and gain. The results of the driver's brain waves become a decision whether the driver is allowed to drive on the highway based on the detected waves (alpha, beta, gamma, theta, and delta) to reduce the chance of an accident. © 2019 IEEE.

关键词： Entropy

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Natural Language Interface to Database (NLIDB) for Decision Support Queries

Natural Language Interface to Database (NLIDB) for Decision ...

引用

2019 International Conference on Data and Software engineering, ICoDSE 2019

作者： Anisyah, Ani Widagdo, Tricya Esterina Nur Azizah, Fazat Institut Teknologi Bandung Magister of Informatics Study Program Bandung Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728149929

Data access that is accurate, fast, and precise is a modern need for analysis or business decision making. NLIDB (Natural Language Interface to Database) is a system that allows users to access information from a database by entering requests that are stated in natural language. The NLIDB for Indonesian language that has been built has not been able to handle decision support data queries. The method of translating imperative sentences into SQL queries is done in two stages. The first stage is identifying the imperative sentence and parsing the sentence into a parsed tree that is assisted by the PC-PATR syntactic parser. The second stage is processing the parse tree into an SQL query. The parse tree is analyzed to look for parts of SQL, then do object searching for SQL parts to ontology. The objects found will be arranged into SQL queries. NLIDB application development is carried out to test the results of the translation of imperative sentences into SQL queries and see the application performance in translating imperative sentences into SQL queries. Application development is done with the Python programming language and MySQL database. The application that was built successfully translates imperative sentences into SQL queries for cross tab data, summary data, trend data, and top-N/bottom-N data. The application performance in carrying out the translation process is quite good, but it is necessary to improve performance for the ontology object searching process on the query generator component. Development that can be done for further research is the handling of sentence forms that have affixes and subquery handling. © 2019 IEEE.

关键词： Decision making

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Natural Language Interface to Database (NLIDB) for Query with Temporal Aspect

Natural Language Interface to Database (NLIDB) for Query wit...

引用

2019 International Conference on Data and Software engineering, ICoDSE 2019

作者： Poetra, Dion Ajie Esterina Widagdo, Tricya Azizah, Fazat Nur Institut Teknologi Bandung Magister of Informatics Study Program Bandung Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728149929

NLIDB (Natural Language Interface to Database) was developed to simplify database access using natural language. In previous studies, NLIDB has been developed which can translate natural languages into several languages, including in Indonesian language. However, there are types of queries that cannot be handled by the system, one of them is a query with a temporal aspect. In this study, we propose a method of translating sentences in Indonesian language that have temporal aspects into SQL queries. The process of translating sentence is performed by identifying the input sentence and parsing the sentence into a parsed tree with PC- PATR as the syntax parser. The parsed tree is analyzed and mapped into parts of SQL. Then, an object search is performed to the ontology that is built from the database to get classes, attributes, operators, and values based on tokens in parts of SQL. Finally, the results of the previous process are organized into SQL queries. The NLIDB application built in this study was successful in translating imperative sentences with temporal aspects in Indonesian language into SQL queries for current state, time slice, sequenced and non-sequenced queries. © 2019 IEEE.

关键词： Database systems

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Testing Real-Time Applications on Windows 10 IOT Using the Nyquist Theory 1

Testing Real-Time Applications on Windows 10 IOT Using the N...

引用

1st International Conference on Computer Science and Applied Mathematic, ICCSAM 2018

作者： Syahputa, Zulfahmi Zarlis, M. Nasution, Beny B. Informatics Engineering Study Program Magister Faculty of Computer Science Information Technology Universitas Sumatera Utara Medan Indonesia

Raspberry Pi is a mini-computer that is provided to carry out activities quickly and precisely, but Raspberry Pi was created to not be able to do the real-time system with the support of Windows 10 IoT operating system, so the real-time system can be done on Raspberry Pi. The real-time applied in the application needs to be tested with the Nyquist theory. The purpose of this study was to get real-time system measurements available on Windows 10 IoT. This test is done using the Nyquist theory by calculating the results of measurements on mp3 streaming performed on Windows 10 IoT. © Published under licence by IOP Publishing Ltd.

关键词： Real time systems

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Entropy and Information Gain Analysis on Low Cost BCI for Motorbike Users to Prevent Accident

Entropy and Information Gain Analysis on Low Cost BCI for Mo...

引用

International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Rolly Maulana Awangga Syafrial Fachri Pane Dinda Majesty Asep Setiawan Moch Yusuf Asyhari Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Magister Program of Informatics Engineering Universitas Islam Indonesia Yogyakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

关键词： Electroencephalography Entropy Turning Tools Calibration Motorcycles

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Natural Language Interface to Database (NLIDB) for Decision Support Queries

Natural Language Interface to Database (NLIDB) for Decision ...

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International Conference on Data and Software engineering (ICODSE)

作者： Ani Anisyah Tricya Esterina Widagdo Fazat Nur Azizah Magister of Informatics Study Program Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9781728149929

ISBN: (纸本)9781728149936

关键词： Structured Query Language Databases Ontologies Market research Natural languages Syntactics

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Natural Language Interface to Database (NLIDB) for Query with Temporal Aspect

Natural Language Interface to Database (NLIDB) for Query wit...

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International Conference on Data and Software engineering (ICODSE)

作者： Dion Ajie Poetra Tricya Esterina Widagdo Fazat Nur Azizah Magister of Informatics Study Program Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9781728149929

ISBN: (纸本)9781728149936

关键词： Ontologies Structured Query Language Natural languages Syntactics Relational databases Software engineering

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