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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Akinola, Akinkunle Atadoga, Akoh Brooks, Kimberlyn Chandna, Vibhuti Green, Robert C. Bowling Green State University Data Science Program Bowling GreenOH United States Bowling Green State University Dept. of Computer Science Bowling GreenOH United States

ISBN: (纸本)9798350320282

To determine whether Ohio college re-opening plans were effective in controlling the spread of COVID-19, cumulative case counts by county were gathered to compare various metrics related to the spread of COVID-19 cases between counties with NCAA colleges and counties without NCAA colleges. Various non-parametric statistical tests were used to determine if the samples were similar, and the analysis found the differences were statistically significant. Metropolitan and non-metropolitan groupings were also added to further subdivide the data set, but the analysis found no statistically significant differences in this case. © 2022 IEEE.

关键词： COVID-19

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TOPOLOGICAL STABILITY AND LATSCHEV-TYPE RECONSTRUCTION THEOREMS FOR CAT(κ) SPACES

arXiv

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arXiv 2024年

作者： Komendarczyk, Rafal Majhi, Sushovan Tran, Will Mathematics Department Tulane University United States Data Science Program George Washington University United States

MSC Codes 55P10 (Primary), 55N31, 54E35 (Secondary)We consider the problem of homotopy-type reconstruction of compact shapes X ⊂ RN that are CAT(κ) in the intrinsic length metric. The reconstructed spaces are in the form of Vietoris–Rips complexes computed from a compact sample S, Hausdorff–close to the unknown shape X. Instead of the Euclidean metric on the sample, our reconstruction technique leverages a path-based metric to compute these complexes. As naturally emerging in the framework of reconstruction, we also study the Gromov–Hausdorff topological stability and finiteness problem for general compact CAT(κ) spaces. Our techniques provide novel sampling conditions as an alternative to the existing and commonly used techniques using weak feature size and µ–reach. In particular, we introduce a new parameter, called the restricted distortion, which is a generalization of the well-known global distortion of embedding. We show examples of Euclidean subspaces, for which the known parameters such as the reach, µ–reach and weak features size vanish, whereas the restricted distortion is finite, making our reconstruction results applicable for such spaces. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics

arXiv

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arXiv 2024年

作者： Olulana, Oluseun Cachel, Kathleen Murai, Fabricio Rundensteiner, Elke Data Science Program Worcester Polytechnic Institute WorcesterMA United States Computer Science Department Worcester Polytechnic Institute WorcesterMA United States

As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://***/sewen007/*** © 2024, CC BY-NC-ND.

关键词： Decision making

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A systematic literature review of machine learning application in COVID-19 medical image classification

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Procedia computer science 2023年 216卷 749-756页

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia. 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.

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

关键词： COVID-19 classification disease machine learning medical images

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Indonesian Agricultural-crops Classification Using Transfer Learning Model

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Procedia Computer science 2023年 227卷 128-136页

作者： Mahmud Isnan Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Count-data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events

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Procedia Computer science 2023年 227卷 805-812页

作者： Alam Ahmad Hidayat Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data.

关键词： count data disaster surveillance floods mixed models Twitter

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META: Deep Learning Pipeline for Detecting Anomalies on Multimodal Vibration Sewage Treatment Plant data 16th

META: Deep Learning Pipeline for Detecting Anomalies on Mult...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Krastev, Simeon Ammartayakun, Aukkawut Jayshankar Mishra, Kewal Koduri, Harika Schuman, Eric Morris, Drew Feng, Yuan Reddy Bandi, Sai Supreeth Ngan, Chun-Kit Yeung, Andrew Li, Jason Ko, Nigel Emdad, Fatemeh Rundensteiner, Elke Ho, Heiton M. H. Wong, T.K. Chan, Jolly P. C. Data Science Program Worcester Polytechnic Institute 100 Institute Rd WorcesterMA United States XTRA Sensing Limited WanChai Hong Kong Drainage Services Department The Government of the Hong Kong Special Administrative Region Wanchai Hong Kong

ISBN: (纸本)9789897587214

In this paper, we propose a hybrid anomaly detection pipeline, META, which integrates Multimodal-feature Extraction (ME) and a Transformer-based Autoencoder (TA) for predictive maintenance of sewage treatment plants. META uses a three-step approach: First, it employs a signal averaging method to remove noise and improve the quality of signals related to pump health. Second, it extracts key signal properties from three vibration directions (Axial, Radial X, Radial Y), fuses them, and performs dimensionality reduction to create a refined PCA feature set. Third, a Transformer-based Autoencoder (TA) learns pump behavior from the PCA features to detect anomalies with high precision. We validate META with an experimental case study at the Stonecutters Island Sewage Treatment Works in Hong Kong, showing it outperforms state-of-the-art methods in metrics like MCC and F1-score. Lastly, we develop a web-based Sewage Pump Monitoring System hosting the META pipeline with an interactive interface for future use. © 2024 by Paper published under CC license (CC BY-NC-ND 4.0)

关键词： Predictive maintenance

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data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors 7

Data mining for epidemiology: The correlation of typhoid fev...

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

作者： Asadi, Faisal Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： data mining

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3D Transfer Learning for Lung Nodule Malignancy Binary Classification

3D Transfer Learning for Lung Nodule Malignancy Binary Class...

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Computer sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Nur Adhianti Heryanto Mahmud Isnan Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Many studies have focused on classifying lung nodules in CT scans, primarily utilizing 2D approaches. However, CT scans are inherently 3D representations, which presents challenges for traditional convolutional neural networks (CNNs) designed for 2D images. In this work, a novel combination of 3D transfer learning and batch oversampling was adopted to classify benign and malignant lung nodules using the Lung Nodule Analysis - Intelligent Systems in Medical Imaging (LUNA22-ISMI) dataset. The preprocessing pipeline consisted of excluding indeterminate labeled nodules, clamping patches to the lung window, extracting nodule regions based on diameter, and applying z-score normalization. Batch oversampling with augmentation was implemented for the training set. Multiple models pre-trained on ImageNet with weights adapted for 3D were evaluated using a five-fold cross-validation setup. Notably, EfficientNetB1 achieved the highest overall and balanced metrics on the test sets, surpassing 90% in sensitivity, specificity, precision, and accuracy, highlighting its potential for binary classification of lung nodules. https://***/nheryanto/luna22

关键词： Training Solid modeling Adaptation models Three-dimensional displays Lungs Computed tomography Transfer learning Windows Cancer Biomedical imaging

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Richie Muljana Syauqi Akmal Deffansyah Hari Andrew Alexander Agung Santoso Gunawan Edy Irwansyah Fabian Surya Pramudya Muhamad Keenan Ario Muhammad Edo Syahputra Said Achmad Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard’s primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang’s districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development.

关键词： Waste management Satellites Local government Urban areas data visualization Systems architecture Production Real-time systems Geospatial analysis Artificial intelligence

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