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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Tokuda, Haruto Bilguunmaa, Myagmardulam Takagi, Noboru Fuse, Yotato Motoyoshi, Tatsuo Sawai, K.E.I. Masuta, Hiroyuki Graduate School of Intelligent Robotics Program Toyama Prefectural University Japan Toyama Prefectural University Department of Data Science Japan Toyama Prefectural University Department of Intelligent Robotics Japan

ISBN: (纸本)9798331528041

Standing in line poses a significant challenge for the visually impaired. Existing queue guidance systems, which recognize line locations and estimate personal orientation, require highspecification computers, making them impractical for everyday use. This study proposes a smartphone-based queue guidance system that leverages the device's camera and sensors to estimate personal orientation efficiently. By optimizing computationally efficient algorithms for smartphone hardware, we aim to create an accessible, cost-effective solution. This system will enhance the independence and mobility of visually impaired individuals, providing real-time guidance to navigate queues seamlessly. Initial testing indicates promising usability and accuracy, paving the way for broader implementation. © 2024 IEEE.

关键词： Smartphones

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Systematic Literature Review on Machine Learning Predictive Models for Indoor Climate in Smart Solar Dryer Dome 4

Systematic Literature Review on Machine Learning Predictive ...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

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

ISBN: (纸本)9781665453950

Precise predictions of indoor climate conditions are required in the implementation of Smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018-2022) on indoor climate prediction which was obtained from research paper databases The output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep Learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to Smart SDD users. Such studies may further be explored to produce more accurate machine learning models. © 2022 IEEE.

关键词： Domes

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Study of bioaerosol disinfection kinetics and application of nonlinear regression modeling for optimization of TiO2-based photocatalytic disinfection process

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Nanotechnology for Environmental Engineering 2023年第4期8卷 911-922页

作者： Nishad, Vartika Mandal, Chinmoy Sahu, Manoranjan Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay 507 Powai Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

Bacteria and viruses are some of the major sources of indoor air pollution. Many strategies are utilized to control indoor biopollutants. Among the new emerging technologies, this work focuses on the photocatalytic oxidation process and application of disinfection models to optimize the photocatalytic process in bioaerosol control. The kinetics of bioaerosol disinfection was investigated in the photocatalytic process with nonlinear regression analysis for Serratia marcescens and E. coli bacteria. The Chick–Watson model was found suitable and was applied for determination of the inactivation kinetics. The inactivation rate constant k’ was found to be 0.0038 min−1 for S. marcescens and 0.09 min−1 for E. coli. The outcomes showed that in the process of disinfection kinetics of bioaerosols, pseudo-first-order kinetics fits well. With nonlinear regression analysis, a model was developed based on functional parameters such as UV intensity (20 to 100 W/m2), relative humidity (30 to 85%) and air velocity (37 to 112 ft/min) for S. marcescens and TiO2 loading (960 and 1516 mg/m2), UV intensity (0.5 to 3.4 mW/cm2) and relative humidity (51 to 85%) for E. coli. For E. coli overall inactivation was found to increase with an increase in TiO2 loading, RH or UV intensities. The inactivation rate of S. marcescens increased with an increase in UV intensity;however, it was found to decrease at very high or low values of relative humidity and air velocity. Hence, the optimum values for relative velocity were found to be 50% and air velocity of 74 ft/min for S. marcescens. The rate expressions derived for both data can be utilized for predicting and optimizing the disinfection rate at different conditions. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Titanium dioxide

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Impact of spatial distribution of light intensity on disinfection kinetics of Mycobacterium smegmatis and E. coli using TiO2-based photocatalyst

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Nanotechnology for Environmental Engineering 2023年第2期8卷 567-579页

作者： Mandal, Chinmoy Nishad, Vartika Sahu, Manoranjan 507 Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Powai Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

Photocatalysis using various nanomaterials of different characteristics has been investigated with various metrics for achieving optimal efficiency. However, the spatial distribution of light intensities from the light source, a key factor in photocatalysis process and the selection of an appropriate kinetic model for explaining disinfection characteristics of different microorganisms have not been given ample consideration. In this study, a suitable model has been applied to identify spatial distribution of light based on the light source used and applied in the kinetic models to identify the disinfection rate. Disinfection efficiencies of nanoparticles (TiO2 and Cu-doped TiO2) with different light intensities on gram-positive bacteria, Mycobacterium smegmatis and gram-negative bacteria E. coli was evaluated. The kinetic study revealed that Mycobacterium smegmatis fits well with Chick–Watson model, while E. coli fits well with delayed Chick–Watson model. The rate constants (k) were found to be 0.013 min−1 (TiO2), 0.021 min−1 (3%Cu–TiO2) and 0.020 min−1 (Cu2+) for Mycobacterium and 0.019 min−1 (TiO2) for E. coli. From model evaluation, light intensity decreased exponentially from 3.07 µW cm−2 (3 cm) to 0.98 µW cm−2 (6 cm) and 0.10 µW cm−2 (25 cm) resulting in a sharp decline in the inactivation potential;however, the effect was insignificant beyond 10 cm. On doubling the intensity, disinfection efficiency increased by 1.6 log CFUml−1 (TiO2) and 2.4 log CFU ml−1 (Cu–TiO2), but with one-fourth of intensity it decreased by 1.2 log CFUml−1 (TiO2) and 1.8 log CFUml−1 (Cu–TiO2). Therefore, by optimizing the combination of nanoparticles like TiO2 and Cu, light intensity at 6.14 µW cm−2 and optimal spatial distribution of the light source at 3 cm, increased efficiency of microbial disinfection could be achieved. Thus, this study provides an approach to optimize photocatalytic parameters with inclusion of spatial distribution of light intensity for enhancing the photocatalyt

关键词： Titanium dioxide

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Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach 3rd

Classification and Prediction of Knee Osteoarthritis by Deep...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Saraswat, Amit Tooley, Tanya Shrivastav, Snigdha Srivastava, Devesh Kumar Department of Electrical Engineering Manipal University Rajasthan Jaipur303007 India Senior Analyst EY Haryana Gurgaon India Master of Science in Data Science program at the University of Rochester New York United States Department of Information Technology Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

The purpose of this research work on medical image analysis is to identify and diagnose knee osteoarthritis (OA) utilizing medical images by applying machine learning techniques. Knee OA is a degenerative joint disease that is becoming more prevalent in aged people worldwide. The study examines the effectiveness of several machine learning algorithms, including deep learning, to analyze medical images of knee OA, such as X-rays and magnetic resonance imaging (MRI). The research findings suggest that using machine learning algorithms might greatly improve the accuracy and efficacy of detecting knee OA, especially in its early stages. Moreover, machine learning algorithms can assist in the creation of specialized treatment plans by predicting the progression of the disease and recommending appropriate remedies. The findings of this study show how machine learning-based medical image analysis has the potential to enhance patient outcomes and quality of life for those with knee OA. Cancer detection diagnosis, neurological condition identification, and cardiovascular function analysis are a few instances of medical picture analysis employing machine learning. Moreover, image-guided treatments have been made more accurate and surgical planning has been made easier with the use of machine learning algorithms. Various methods are applied to achieve a test accuracy of 76.10% which is just 5% less than the accuracy provided by the Kellen-Lawrence Grading Scale. Overall, machine learning-based medical image analysis has the potential to revolutionize medical practice by facilitating earlier and more precise diagnosis, individualized treatment planning, and improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC

Multi-layer network modelling of genomic and transcriptomic ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mascolo, Davide Farina, Lorenzo Petti, Manuela Sapienza University of Rome Program in Data Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350337488

Recent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients (p-value ≤ 0.05) which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy. © 2023 IEEE.

关键词： immunotherapy multi-layer network Network-based omics integration non-small cell lung cancer patient Stratification

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants 8

Utilizing Semi-supervised Method in Predicting BRCA1 Pathoge...

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

作者： Hidayat, Alam Ahmad Trinugroho, Joko Pebrianto Nirwantono, Rudi Sudigyo, Digdo Pardamean, Bens Department of Mathematics School of Computer Science Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their clinical consequences on breast cancer. Machine learning models can be applied to predict the landscape of protein variant effects that might not be always accessible by experiments. In this work, we propose a simple semi-supervised learning method using a Gaussian mixture model to predict ~90% of the unlabeled missense variants of the BRCA1 gene collected from the ClinVar database. High-quality embeddings are used as a feature of the protein sequences, extracted using the latest pre-trained transformer-based language protein model. A statistical test show that the protein embeddings are effective and robust for predicting pathogenicity. Further, the lower representations of the features are then fed into the semi-supervised model. The prediction performance of the model only for the labeled testing data achieves an AUC score and an accuracy of 79.27% and 71.58%, respectively. Using our defined pathogenic probability score, we find that ~94% of variants in our unlabeled dataset are well-separated into either benign or pathogenic classes according to that scoring. Our scores obtain a moderate Spearman rank correlation with the results of established unsupervised variant effect models. Finally, our approach can potentially be developed for more accurate and biologically reliable predictions of the variant effects. © 2023 The Authors. Published by Elsevier B.V.

关键词： embeddings language model mutation effect protein sequence semi-supervised learning

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A review: data pre-processing techniques used for diabetes prediction

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Procedia Computer science 2024年 245卷 667-676页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cmputer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification.

关键词： Diabetes Classification data Pre-Processing Machine Learning Imbalanced dataset

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Machine Learning Approach for Single Nucleotide Polymorphism Selection in Genetic Testing Results 8

Machine Learning Approach for Single Nucleotide Polymorphism...

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

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Isnan, Mahmud Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Lactose intolerance is a type of digestive problem that may threaten the population because milk and dairy products compose of nutrients that are essential for human body. Genetic tests possess a great potential to detect lactose intolerance as it can be used in children and even infants. However, a new approach to analyze the genetic test results is needed to elucidate the Single Nucleotide Polymorphisms (SNPs) that are related to lactose intolerance. In this work, we utilized the machine learning based feature selection to select the SNPs associated with lactose tolerance trait from genotyping samples of direct-to-customer (DTCG genetic tests, obtained from the public database. Recursive Feature Elimination (RFE) with XGBoost model was used to perform feature selection. We also compared three different models, such as XGBoost, support vector machine (SVM), and random forest (RF) for training the selected features. Our findings revealed that 20 SNPs (out of 3501) were chosen, with rs4394668 as the most important variables (F-score 0.009). Furthermore, when compared to the RF and SVM models, the XGBoost model had the highest accuracy (0.87). Further studies should be undertaken to elucidate how the selected SNPs may lead to the lactose intolerance trait. © 2023 The Authors. Published by Elsevier B.V.

关键词： feature selection genetic test lactose intolerance SNP

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2 9th

An Exploratory Computational Approach for Elucidating a Nons...

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9th International Conference on Computer science and Computational Intelligence, ICCSCI 2024

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University 11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings. © 2024 The Authors.

关键词： Molecular docking

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