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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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A UNIFIED MODEL FOR ZERO-SHOT SINGING VOICE CONVERSION AND SYNTHESIS 23

A UNIFIED MODEL FOR ZERO-SHOT SINGING VOICE CONVERSION AND S...

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23rd International Society for Music Information Retrieval Conference, ISMIR 2022

作者： Wu, Jui-Te Wang, Jun-You Jang, Jyh-Shing Roger Su, Li NTU-AS Data Science Degree Program National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9781732729926

Recent advances in deep learning not only facilitate the implementation of zero-shot singing voice synthesis (SVS) and singing voice conversion (SVC) tasks but also provide the opportunity to unify these two tasks into one generalized model. In this paper, we propose such a model that generate the singing voice of any target singer from any source singing content in either text or audio format. The model incorporates self-supervised joint training of the phonetic encoder and the acoustic encoder, with an audio-to-phoneme alignment process in each training step, such that these encoders map the audio and text data respectively into a shared, temporally aligned, and singer-agnostic latent space. The target singer’s latent representations encoded at different granularity levels are all trained to match the source latent representations sequentially with the attention mechanisms in the decoding stage. This enables the model to generate unseen target singer’s voice with fine-grained resolution from either text or audio sources. Both objective and subjective experiments confirmed that the proposed model is competitive with the state-of-the-art SVC and SVS methods. © J.-T. Wu, J.-Y. Wang, J.-S. R. Jang and L. Su.

关键词： Deep learning

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Mapping Emergency Medicine data to the Observational Medical Outcomes Partnership Common data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency data Registry

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JACEP Open 2025年第1期6卷 100016-100016页

作者： Cohen, Inessa Diao, Zihan Goyal, Pawan Gupta, Aarti Hawk, Kathryn Malcom, Bill Malicki, Caitlin Sharma, Dhruv Sweeney, Brian Weiner, Scott G. Venkatesh, Arjun Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States American College of Emergency Physicians Washington DC United States Department of Emergency Medicine Brigham and Women's Hospital Boston MA United States Center for Outcomes Research and Evaluation (CORE) Section of Cardiovascular Medicine Yale School of Medicine New Haven CT United States

Objectives: This study aims to conduct a gap analysis to determine the feasibility of mapping electronic health record data from the Clinical Emergency data Registry (CEDR) to the Observational Medical Outcomes Partnership Common data Model (OMOP-CDM). Methods: We employed a structured approach using a custom-built comparison matrix. This matrix facilitated the alignment of CEDR data fields with the corresponding elements in the OMOP-CDM schema. Each field was evaluated for compatibility, with categorization into 3 distinct types: direct matches, fields requiring transformation, and fields with no OMOP-CDM equivalent. The mapping process was informed by consultations with the Observational Health data sciences and Informatics community forums and was guided by existing documentation and best practices in data harmonization. We performed descriptive analyses, quantifying the extent of direct matches and identifying the specific transformations needed for each CEDR-CDM field to ensure compliance with the OMOP-CDM model. Results: Our analysis indicates a high degree of compatibility between CEDR and OMOP, with over 90% (244/269) of CEDR fields being successfully mapped. Specifically, 173 fields had direct matches, whereas 71 required transformations. Challenges identified include addressing fields unique to CEDR with no OMOP-CDM equivalent and managing the transformations required for proper alignment. Conclusion: The OMOP-CDM presents a promising framework for standardizing emergency medicine data, thereby enhancing future query automation, analytics, and cross-institutional collaboration. Despite the potential challenges in capturing unique CEDR fields and addressing necessary transformations, most emergency department data can be standardized within the OMOP-CDM, fostering broader insights and applications in research and public health. © 2024 The Author(s)

关键词： databases electronic health records emergency service informatics opioid/adverse effects quality of health care registries

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Uncertainty abounds, what now?

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science 2025年第6740期387卷 1261页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies The reviewer is at the Harry Radzyner Law School The reviewer is at the Dina Recanati School of Medicine Reichman University Herzliya Israel The reviewer is at the Department of Biomedical Informatics and Data Science The reviewer is at the Program in Computational Biology and Bioinformatics The reviewer is at the Department of Computer Science Yale University New Haven CT USA.

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A review: data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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

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 Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 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. © 2024 The Authors.

关键词： Adversarial machine learning

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CREASE-2D Analysis of Small Angle X-ray Scattering data from Supramolecular Dipeptide Systems

arXiv

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arXiv 2025年

作者： Gupta, Nitant Akepati, Sri V.V.R. Bianco, Simona Shah, Jay Adams, Dave J. Jayaraman, Arthi Department of Chemical and Biomolecular Engineering University of Delaware NewarkDE19716 United States Data Science Program University of Delaware NewarkDE19716 United States School of Chemistry University of Glasgow GlasgowG12 8QQ United Kingdom Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States

In this paper, we extend a recently developed machine-learning (ML) based CREASE-2D method to analyze the entire two-dimensional (2D) scattering pattern obtained from small angle X-ray scattering measurements of supramolecular dipeptide micellar systems. Traditional analysis of such scattering data would involve use of approximate or incorrect analytical models to fit to azimuthally-averaged 1D scattering patterns that can miss the anisotropic arrangements. Analysis of the 2D scattering profiles of such micellar solutions using CREASE-2D allows us to understand both isotropic and anisotropic structural arrangements that are present in these systems of assembled dipeptides in water and in the presence of added solvents/salts. CREASE-2D outputs distributions of relevant structural features including ones that cannot be identified with existing analytical models (e.g., assembled tubes’ cross-sectional eccentricity, tortuosity, orientational order). The representative three-dimensional (3D) real-space structures for the optimized values of these structural features further facilitate visualization of the structures. Through this detailed interpretation of these 2D SAXS profiles we are able to characterize the shapes of the assembled tube structures as a function of dipeptide chemistry, solution conditions with varying salts and solvents, and relative concentrations of all components. This paper demonstrates how CREASE-2D analysis of entire SAXS profiles can provide an unprecedented level of understanding of structural arrangements which has not been possible through traditional analytical model fits to the 1D SAXS data. © 2025, CC BY-NC-SA.

关键词： X ray scattering

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Applying Parallel Processing to Improve the Computation Speed of K-Nearest Neighbor Algorithm

Applying Parallel Processing to Improve the Computation Spee...

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2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Alqaddoumi, Abdulla University of Bahrain Collage of Science Big Data Science and Analytics Master Program Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

K-Nearest Neighbor (KNN) is a widely used algorithm to gain an accurate and efficient classification. One of the drawbacks of the algorithm is the time required to calculate the distance for each point. In this paper, the aim is to speed up the KNN algorithm with the implementation of multi threads and multiprocessors to reduce the time to execute the algorithm. There are two medical datasets used to apply the KNN algorithm for the comparison of the sequential and parallel performance. The datasets utilized are Heart Test and Breast Cancer Wisconsin (Diagnostic) data Sets. The multiprocessing is used to parallelize the computation of the distance of KNN. The parallel KNN outperforms the sequential version with the speedup increase, leading to reduction in time for both the processes and threads. © 2020 IEEE.

关键词： Learning algorithms

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

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2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Homeed, Mawra T. Mohamed, Zahra S. Zeki, Ahmed M. University of Bahrain Big Data Science Analytics Master Program Collage of Science Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others. © 2020 IEEE.

关键词： Decision trees

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Characterization and Classification of Purity of Limestone in Madura Island for Industrial Application 1

Characterization and Classification of Purity of Limestone i...

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1st International Symposium on Physics and Applications, ISPA 2020

作者： Munawaroh, F. Muharrami, L.K. Triwikantoro Arifin, Z. Natural Science Education Study Program Universitas Trunojoyo Madura Bangkalan Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The characterization and classification of purity of limestone at Madura Island was investigated. Sampling was taken from nine quarries from different areas. The chemical analysis was carried out by X-ray fluorescence (XRF);the crystalline phase was characterized by X-ray diffraction (XRD). Analysis of purity classification based on XRF and XRD results according to British Geological Survey. Limestone has different purity, there are very high, medium, low and impure purity. The sample has a very high purity from Pamekasan (P.1) with CaO content of 99.06 wt% and 100% calcite. The samples are medium purity from Bangkalan (B.1, B.3), Pamekasan (P.2), Sumenep (S.1, and S.3) with CaO content of 91.61- 93.67 wt%. The samples are low purity from S.2 with CaO content 85.79 wt% and 100% dolomite. And the sample has impurities from P.3 and B.2 with CaO content 82.3 wt% and 84.7 wt%. Limestone with very high and medium purity which is processed into PCC can be applied in many industries as filler in rubber, paper making, plastic, paint, food, pharmaceutical, ceramic, adhesives, sealants, agriculture, and animal feed. © Published under licence by IOP Publishing Ltd.

关键词： Limestone

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Forecasting Smart Meter Energy Usage Using Distributed Systems and Machine Learning

Forecasting Smart Meter Energy Usage Using Distributed Syste...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Chris Dong Lingzhi Du Feiran Ji Zizhen Song Yuedi Zheng Alexander Howard Paul Intrevado Diane Myung-kyung Woodbridge Alexander J. Howard Data Science Program University of San Francisco Data Science ProgramUniversity of San Francisco

In this research, we explore the technical and computational merits of a machine learning algorithm on a large data set, employing distributed systems. Using 167 million (10 GB) energy consumption observations collected by smart meters from residential consumers in London, England, we predict future residential energy consumption using a Random Forest machine learning algorithm. Distributed systems such as AWS S3 and EMR, MongoDB and Apache Spark are used. Computational times and predictive accuracy are evaluated. We conclude that there are significant computational advantages to using distributed systems when applying machine learning algorithms on large-scale data. We also observe that distributed systems can be computationally burdensome when the amount of data being processed is below a threshold at which it can leverage the computational efficiencies provided by distributed systems.

关键词： Sparks Energy consumption Distributed databases Smart meters Machine learning Machine learning algorithms Meteorology

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