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Machine Learning Approaches in Detecting Autism Spectrum Disorder

Procedia computer science 2023年 227卷 1070-1076页

作者： Daniel Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Smart village design using enterprise architecture framework model 6

Smart village design using enterprise architecture framework...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Faisal Suharjito Bina Nusantara University BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

This paper describes the design of smart village that can be describe as an indicator for the smart village in Indonesia, where the Indonesian population is now more than 250 million peoples. with several islands as much as 17, 504 islands, consisting of 53.3% were in urban areas, the rest spread in rural areas, but it will turn around if there is no solution currently to address the disparity in the village in terms of infrastructure and technology. One of the concepts can be applied to overcome these problems is "Smart Village". Conducting a study of four villages on the island of Java in Indonesia, which was awarded the village of Information Technology, the study aims to research the indicators that make the village as the village of Information Technology. By using the TOGAF Enterprise Architecture analysis, the result of study are the components that exist in the ICT village winners and what the other components needs to complement the design of smart village which can be a reference in Indonesia in future. © 2021 IEEE.

关键词： Rural areas

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text

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Procedia computer science 2024年 245卷 202-209页

作者： Felix Indra Kurniadi Ni Luh Putu Satyaning Pradnya Paramita Erna Fransisca Angela Sihotang Maria Susan Anggreainy Rongfang Zhang Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Department of Rehabilitation and Health Service University of North Texas

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection.

关键词： depression natural language processing BERT RoBERTa

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Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

Restaurant Customer Feedback Sentiment Analysis using Aspect...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Indonesia Computer Science Department BINUS Online Learning Jakarta Indonesia Information Systems Department BINUS Online Learning Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively.

关键词： Training computer science Sentiment analysis Analytical models Computational modeling Machine learning Predictive models

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Comparison of Gaussian Hidden Markov Model and Convolutional Neural Network in Sign Language Recognition System 1

Comparison of Gaussian Hidden Markov Model and Convolutional...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Gunawan, Herman Suharjito Pratama, Devriady Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Sign language Recognition is the study to help bridging communication of deaf-mute people. Sign Language Recognition uses techniques to convert gestures of sign language into words or alphabet. In Indonesia, there are two types of sign languages which are used, Bahasa Isyarat Indonesia (BISINDO) and Sistem Isyarat Bahasa Indonesia (SIBI). The purpose of this research is comparing sign language recognition methods between Gaussian Hidden Markov Model and Convolutional Neural Network using indonesian sign language SIBI as a dataset. The dataset comes from 200 videos from 2 signers. Each signer performs 10 signs with 10 repetitions. To improve the recognition accuracy, modified histogram equalization is used as an image enhancement. Skin detection was used to track the movement of the gesture as input features in the Gaussian Hidden Markov Model and fine tuning was used in Convolutional Neural Network using transfer learning, freeze layer, and dropout. The results of the research are the Gaussian Hidden Markov Model provides accuracy value of 84.6% and Convolutional Neural Network provides accuracy value of 82%. © 2021 IEEE.

关键词： Hidden Markov models

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Energy Sector Stock Price Prediction Using The CNN, GRU & LSTM Hybrid Algorithm

Energy Sector Stock Price Prediction Using The CNN, GRU & LS...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Bambang Sulistio Harco Leslie Hendric Spits Warnars Ford Lumban Gaol Benfano Soewito Computer Science Department Binus Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks..

关键词： Deep learning computer science Recurrent neural networks Sociology Prediction methods Prediction algorithms Convolutional neural networks

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The effect of visual advanced driver assistance systems on a following human driver in a mixed-Traffic condition 7

The effect of visual advanced driver assistance systems on a...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Ariansyah, Dedy Pardamean, Bens Caruso, Giandomenico Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers' ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-Traffic condition. © 2022 Elsevier B.V.. All rights reserved.

关键词： Advanced driver assistance systems

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Prediction of Oil Palm Conditions Using Deep Learning Based on the Visible Atmospherically Resistant Index on UAV Imagery

Prediction of Oil Palm Conditions Using Deep Learning Based ...

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Informatics Engineering, science & Technology (INCITEST), International Conference on

作者： Prasetyo Mimboro Benfano Soewito Haryono Soeparno Widodo Budiharto Computer Science Departement BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia

Artificial neural network (ANN)-based computer vision techniques are becoming increasingly popular for palm oil disease detection and classification. Deep learning models' capacity to automatically learn and extract relevant image features has enabled accurate and efficient detection and classification of palm oil diseases. In this research, research was conducted to test the deep learning method to predict the condition of oil palm plantations based on the visible atmospherically resistant index on the Unmanned Aerial Vehicle Image. Some diseases that can attack oil palm trees are root disease or oil palm root rot (blast disease), basal stem rot (ganoderma), bud rot (spear rot), yellow line disease (patch yellow). This study aims to predict the condition of oil palm trees based on the VARI so that the process of detecting the spread of disease in oil palm trees can be accelerated. In this study, the prediction model for the condition of oil palm trees using the ANN algorithm succeeded in predicting the condition of oil palm trees and provided satisfactory prediction results, namely an accuracy rate of 94.7% and a loss of 21.58%.

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Tourism recommender system using hybrid multi-criteria approach 2

Tourism recommender system using hybrid multi-criteria appro...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Maru'Ao, M.S.P. Suharjito Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

Traveling has become a part of life at the time of the current urbanization. The growth rate of the internet and the availability of information allows travellers to access tourist information easier and faster. However, the individual's ability to find the information that they want is not proportional to the complexity of the information. This research would like to develop a reliable tourism recommender system that is able to provide destination recommendations according to user preferences with a combination of several methods i.e., Content-based, Collaborative Filtering, Multi-Criteria ratings, Demographic and Ontology-based. This research aims to implement a recommendations system for tourism through mobile devices by using the Android operating system. The results of this research indicate that the Hybrid Multi-Criteria method is able to provide recommendations to the user in accordance with the user's personal preferences and history of previous user rating with the average precision is 0.7. Evaluation metric of each methods indicate the lowest RMSE 0.84 in Multi-Criteria. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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A bibliometric analysis on e-government and tourism 6

A bibliometric analysis on e-government and tourism

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Nindito, Hendro Prabowo, Harjanto Leslie Hendric, Spits Warnars Harco Sfenrianto Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

E-Government and Tourism are fields of research that are constantly evolving. Where tourism services are one of the major foreign exchange earners for most countries. Governments in various countries are trying their best in providing services by utilizing information technology. The purpose of this research is to find out and map the scientific publications that have been carried out on the topics of e-government and tourism indexed by Scopus scientific publications, published from 2000 to 2020. So that research trends in this field can be seen and provide insights for further research. This research uses the bibliometric analysis method. Results showed that the most cited research topic was the recommendation system topic with 639 citations, the country that contributed the most documents was China and the most affiliated contributors were Georgia College State universities and Sydney University of Technology with each producing four articles. This study also contributes to knowledge by proposing classification characteristics of egovernment and tourism with the theme: Websites, Applications, Services, and Electronics. This characteristic abbreviated as WASE. © 2021 IEEE.

关键词： e-government

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