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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Bioinformatics Graduate Program Department of Computer Science Boston University United States Department of Computer Science Tel Aviv University

AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a

关键词： Biological organs

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias

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Procedia Computer Science 2024年 245卷 109-116页

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

Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases.

关键词： Malignant Ventricular Arrhythmia Sudden Cardiac Death Variational Quantum Algorithm Quantum Computing Neural Network

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Digital Twin (DT) Smart City for Air Quality Management 8

Digital Twin (DT) Smart City for Air Quality Management

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Ariansyah, Dedy Isnan, Mahmud Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality Digital Twin Mixed Reality Smart City

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Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Pardamean, Bens Suparyanto, Teddy Sinaga, Gokma Sahat Tua Elwirehardja, Gregorius Natanael Firmansyah, Erick Ginting, Candra Mawandha, Hangger Gahara Putra, Dian Pratama Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta55282 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification 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. © 2023 The Authors. Published by Elsevier B.V.

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

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Methacrylic acid/Tween 80 engineered amorphous calcium phosphate as an effective bioactive reinforcing nanofillers in dental adhesive

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Dental Materials Journal 2025年第2期44卷 190-200页

作者： Maimansomsuk, Siripatsorn Teangrom, Paremai Teanchai, Chayada Sinthuvanich, Chomdao Ruengket, Pakorn Attarataya, Jakrada Akkarachaneeyakorn, Khrongkhwan Department of Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Center of Excellence for Innovation in Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Research Center Faculty of Dentistry Mahidol University Bangkok10400 Thailand Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand Faculty of Science Kasetsart University Bangkok Thailand Genetic Engineering and Bioinformatics Program Graduate School Kasetsart University Bangkok Thailand Nakhon Ratchasima30000 Thailand

Bioactive fillers in dental adhesives are designed to release beneficial ions, such as calcium and phosphate, to help in remineralization process, and preventing secondary caries. Their incorporation also aims to improve the longevity of dental restorations. Herein, CaP nanoparticles were synthesized through a water-in-oil emulsion method using Tween80 as a surfactant and methacrylic acid (MAA). MAA officiated as a co-surfactant and reduced the size of CaP nanoparticle, including improved compatibility with the components of dental adhesive. The incorporation of 2% T80/CaP/MAA nanofillers into dental adhesive demonstrated increased efficiency, as evidenced by a higher degree of conversion (%DC) and greater micro-tensile bond strength (μTBS). More importantly, T80/CaP/ MAA nanofillers were able to induce remineralization process in the dental structure, which was confirmed by EDX mapping and IR imaging. Finally, a cytotoxicity assessment of the dental adhesive containing T80/CaP/MAA showed no significant adverse effects on human fibroblast cells. © 2025, Japanese Society for Dental Materials and Devices. All rights reserved.

关键词： Stem cells

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Enhancing Interoperability of Digital Twin in the Maintenance phase of Lifecycle 6

Enhancing Interoperability of Digital Twin in the Maintenanc...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Ariansyah, Dedy Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Centre Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

In today's digitalization era, asset management has evolved following the rapid adoption of information systems and digital technologies. In the maintenance phase of the asset, Digital Twin (DT) emerges as an effective approach to improve failure recognition and structural damage prediction through DT-driven Prognostic and Health Monitoring (PHM). As a result, DT allows asset manager to proactively plan maintenance strategy to improve the availability of the assets. However, to reduce asset downtime in a cost-effective and time efficient manner, DT for maintenance phase needs to be integrated with other software systems that manage the provision of maintenance service and spare part such as computerized maintenance management system (CMMS) or Enterprise Resource Planning (ERP). Nevertheless, many of the developed DT in PHM were not equipped with semantic data model and hence, lacks interoperability with other systems (e.g., other DT or non-DT system) and limits the effective use of the DT. To qualify as an effective DT, it needs a semantical-enabled interface to connect with data and models from various domain to enable DT-driven decision-making. This paper leverages a rule-based ontology reasoning model to enhance DT interoperability with other maintenance software systems. The results depicts that the developed model paves a way for an effective use of DT-driven system in asset maintenance management. © 2022 IEEE.

关键词： CMMS Digital Twin ERP Maintenance Ontology

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Sequence to Sequence Deep Learning Architecture for Forecasting Temperature and Humidity inside Closed Space 10

Sequence to Sequence Deep Learning Architecture for Forecast...

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10th International Conference on Cyber and IT Service Management, CITSM 2022

作者： Setiawan, Karli Eka Elwirehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (数字)9781665460743

ISBN: (纸本)9781665460743

Solar Dryer Dome (SDD), an agricultural facility for drying and preserving agricultural products, needs a smart ability to predict the future indoor climate accurately, including indoor temperature and indoor humidity, in order to optimize electricity usage. To overcome these challenges, deep learning has been a widely adopted method. This research aims to forecast the future indoor climate using time series data by implementing a sequence-to-sequence (seq2seq) architecture, which is mostly used in Natural Language Processing (NLP) tasks. The two proposed seq2seq models, Long Short-Term Memory (LSTM) seq2seq and Gated Recurrent Unit (GRU) seq2seq, have proven to be superior to the adapted LSTM and GRU. The results show that the seq2seq GRU model outperforms the adapted GRU baseline model by an average difference of 0.03013 in MAE and the seq2seq LSTM model outperforms the adapted LSTM baseline model by an average difference of 0.00941 in MAE. To the best of our knowledge, this is the first implementation of seq2seq models for indoor climate forecasting on the Room Climate dataset. © 2022 IEEE.

关键词： Long short-term memory

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