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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Portillo, Janeth Dominguez Peralta, Laura M. Rodriguez Sampaio, Paulo N. M. Nunes, Eldman De Oliveira Faculty of Information Technologies and Data Science Mexico Graduate Program in Computing and Systems Bahia Salvador Brazil

ISBN: (纸本)9789893347928

The environmental indicators provide significant information in order to describe the state of a phenomenon, environment or area [1]. This paper addresses the indicators present in the air and their relationship in the accommodation and transmission of viruses, as well as how these environmental indicators can lead to health problems. Therefore, the main goal of this work is to review the scientific contributions in the literature in order to determine and select the potential environmental indicators present in indoor environments which have an impact on the health of people who work and live within these spaces. For this purpose, we carried out a Systematic Literature Review (SLR) related to environmental monitoring, COVID-19, Internet of things, air pollution and environmental indicators within indoor spaces. This SLR allowed us to identify indicators that according to their level of presence can represent a high risk for people's health. Therefore, the process of planning, conducting and reporting the Systematic Literature Review enabled us not only to identify some environmental indicators, but also their corresponding monitoring advantages and disadvantages, and multiple perspectives and approaches of each contribution in the literature. Further on, this study will be the basis for the proposal of an automatic and intelligent platform based on Internet of Things for monitoring indoor environments in order to detect high risk levels for the contamination of contagious diseases. © 2023 ITMA.

关键词： COVID-19

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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

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

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： Computer vision

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Correction to: A machine learning approach to leveraging electronic health records for enhanced omics analysis (Nature Machine Intelligence, (2025), 7, 2, (293-306), 10.1038/s42256-024-00974-9)

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Nature Machine Intelligence 2025年

作者： Mataraso, Samson J. Espinosa, Camilo A. Seong, David Reincke, S. Momsen Berson, Eloise Reiss, Jonathan D. Kim, Yeasul Ghanem, Marc Shu, Chi-Hung James, Tomin Tan, Yuqi Shome, Sayane Stelzer, Ina A. Feyaerts, Dorien Wong, Ronald J. Shaw, Gary M. Angst, Martin S. Gaudilliere, Brice Stevenson, David K. Aghaeepour, Nima Department of Anesthesiology Perioperative and Pain Medicine Stanford University School of Medicine StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Immunology Program Stanford University School of Medicine StanfordCA United States Medical Scientist Training Program Stanford University School of Medicine StanfordCA United States Department of Pathology Stanford University School of Medicine StanfordCA United States Department of Microbiology and Immunology Stanford University School of Medicine StanfordCA United States Department of Pathology University of California San Diego La Jolla CA United States

Correction to: Nature Machine Intelligencehttps://***/10.1038/s42256-024-00974-9, published online 16 January 2025. In the version of the article initially published, in the "COMET deep learning architecture" section of the Methods, the equations for MSE and BSE were switched and have now been corrected as shown: (Formula presented.) and (Formula presented.) Additionally, the grey background circles in Fig. 4d were incorrectly offset from the clusters and have now been corrected. These corrections have been made to the HTML and PDF versions of the article. © The Author(s) 2025.

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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity 24

An Enhanced BERTopic Framework and Algorithm for Improving T...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Sawant, Sahil Yu, Jinhong Pandya, Kirtikumar Ngan, Chun-Kit Bardeli, Rolf Data Science Program Worcester Polytechnic Institute WorcesterMA United States Big Data and Consumer Analytics Vodafone GmbH NRW Duesseldorf Germany

ISBN: (纸本)9798350319934

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets. © 2022 IEEE.

关键词： Embeddings

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HORSE: Harmonize regional and global stratigraphic records through horizon sequencing

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Palaeogeography, Palaeoclimatology, Palaeoecology 2025年 670卷

作者： Chu, Tianyi Huang, Hanhui Fan, Junxuan Deng, Yiying Xu, Tao Qian, Chao Xue, Ke Sheets, H. David Stephenson, Michael H. Shi, Yukun Hou, Xudong School of Earth Sciences and Engineering and Frontiers Science Center for Critical Earth Material Cycling Nanjing University Nanjing 210023 China State Key Laboratory of Critical Earth Material Cycling and Mineral Deposits Nanjing University Nanjing 210023 China Department of Earth Sciences University of Oxford South Parks Road Oxford OX1 3AN United Kingdom School of Resources and Environmental Engineering Hefei University of Technology Hefei 230009 China HIC-ZJU Sunny Innovation Institute ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 311200 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China School of Artificial Intelligence Nanjing University Nanjing 210023 China Data Science Program Department of Computer and Data Science Merrimack College North Andover 01845 MA United States Stephenson Geoscience Consulting Ltd. Keyworth Nottingham NG125HU United Kingdom

A uniform, high-resolution geological timescale is essential for studying Earth's history, including the dynamics of biodiversity and environmental change. Quantitative stratigraphy combines stratigraphic data with statistical and computational approaches into a global timescale that allows them to be correlated simultaneously. For example, Constrained Optimization (CONOP), built upon Graphic Correlation, sequences geological events to generate a composite sequence by resolving inconsistencies among stratigraphic records. However, CONOP determines only the global order of events (e.g., first or last appearances of species) and cannot assign ages to local records, e.g., a locally observed fossil occurrence. Horizon Annealing (HA) addresses this by using a simulated annealing algorithm to sequence sampling “horizons” while preserving local stratigraphic details. Here, we report HORizon SEquencing (HORSE), a generalized and optimized method for HA, with an implementation including parallel computing and genetic algorithms to enable fast, automatic stratigraphic correlation on large datasets. We evaluate HORSE, HA, and CONOP on three datasets—two empirical and one simulated—and assess their performance in terms of accuracy, efficiency, and robustness. HORSE greatly outperforms HA in computational efficiency and performs comparably to CONOP in event sequencing with greater robustness. Beyond constructing high-resolution geological timescales or life histories in deep time, HORSE uniquely preserves local stratigraphic information, enabling applications in palaeogeographical or palaeoecological studies, as well as evaluations of preservation and sampling biases—capabilities not possible with CONOP. © 2025 Elsevier B.V.

关键词： Constrained Optimization Geological timescale Horizon Annealing Parallelization Quantitative stratigraphy

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical Engineering and Computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias 9th

Variational quantum neural network with optimized ansatz for...

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

作者： Dominic, Nicholas Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 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. © 2024 The Authors.

关键词： Quantum computers

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ADAM-1: AI and Bioinformatics for Alzheimer’s Detection and Microbiome-Clinical data Integrations

arXiv

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arXiv 2025年

作者： Huang, Ziyuan Sekhon, Vishaldeep Kaur Guo, Ouyang Newman, Mark Sadeghian, Roozbeh Vaida, Maria L. Jo, Cynthia Ward, Doyle Bucci, Vanni Haran, John P. Department of Microbiology UMass Chan Medical School WorcesterMA01655 United States Department of Geriatric Medicine and Gerontology Johns Hopkins University BaltimoreMD21218 United States Data Sciences Harrisburg University of Science and Technology HarrisburgPA17101 United States DevIS Innovation Labs DevIS LLC ArlingtonVA22201 United States Department of Emergency Medicine UMass Chan Medical School WorcesterMA01655 United States Program in Microbiome Dynamics UMass Chan Medical School WorcesterMA01655 United States

The Alzheimer’s Disease Analysis Model Generation 1 (ADAM-1) is a multi-agent large language model (LLM) framework designed to integrate and analyze multi-modal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and detection of Alzheimer’s disease (AD). By leveraging retrieval-augmented generation (RAG) techniques along with its multi-agent architecture, ADAM-1 synthesizes insights from diverse data sources and contextualizes findings using literature-driven evidence. Comparative evaluation against XGBoost revealed similar mean F1 scores but significantly reduced variance for ADAM-1, highlighting its robustness and consistency, particularly in small laboratory datasets. While currently tailored for binary classification tasks, future iterations aim to incorporate additional data modalities, such as neuroimaging and biomarkers, to broaden its scalability and applicability for Alzheimer’s research and *** Codes 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Classification (of information)

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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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