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An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis 25

An Integrated Framework for Team Formation and Winner Predic...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Galbiati, Federico Gran, Ranier X. Jacques, Brendan D. Mulhern, Sullivan J. Ngan, Chun-Kit Worcester Polytechnic Institute Department of Computer Science Worcester United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350330014

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy. © 2023 IEEE.

关键词： Predictive analytics

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LDA Topic Modeling for Bioinformatics Terms in arXiv Documents 9th

LDA Topic Modeling for Bioinformatics Terms in arXiv Documen...

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

作者： Karnyoto, Andrea Stevens Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

A wide variety of disciplines contribute to bioinformatics research, including computer science, biology, chemistry, mathematics, and physics. This study determines the number of research articles published on arXiv classified as bioinformatics topics and the most frequently used bioinformatics terms using topic modeling, Latent Dirichlet Allocation (LDA). An algorithm based on LDA is used to discover topics hidden within large collections of documents through the use of statistical analysis. Our research examined 226453 articles on arXiv between January 2023 and January 2024. As a result, there are more than 10521 articles categorized into bioinformatics topics. Most commonly, 6352 documents are in the "Mathematical Physics" category. The second most popular category is "Computer science," with 2950 documents. Accordingly, the terms 'RNA,' 'sequence,' 'tree,' and 'homology' are the three most commonly used terms in bioinformatics. The study of RNA plays a vital role in molecular biology;thus, the study of RNA is prevalent in bioinformatics. Sequential data refer to the order in which nucleotides or amino acids can be found in a DNA molecule or a protein. © 2024 The Authors.

关键词： arXiv bioinformatics Latent Dirichlet Allocation

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

引用

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

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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Determining environmental indicators related to the propagation of contagious diseases and health issues: A Systematic Literature Review 18

Determining environmental indicators related to the propagat...

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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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Federated Approach to Online Pairwise Comparison Ranking Problem

Federated Approach to Online Pairwise Comparison Ranking Pro...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Lin, Tse-Yu Tsai, Yen-Lung National Taiwan University Data Science Degree Program Taipei Taiwan National Chengchi University Department of Mathematical Sciences Taipei Taiwan

ISBN: (纸本)9798350324136

In this work, we propose a federated learning (FL) framework for the online HodgeRank problem to obtain a global ranking based on the pairwise comparison data provided by users while respecting users' data privacy. HodgeRank is a statistical ranking method that views pairwise comparison data as an edge-weighted directed graph, and, vertex weight function can be viewed as a ranking that recovers edge weight using difference. A critical assumption of the HodgeRank is the connectivity of the comparison graph. This assumption relies on the contribution of users' data to form a weakly connected directed graph between items to be ranked. In this paper, we aim to find a framework to compute HodgeRank with the minimum usage of users' data. © 2024 IEEE.

关键词： federated learning HodgeRank pairwise comparison

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Salary Classification & Prediction based on Job Field and Location using Ensemble Methods

Salary Classification & Prediction based on Job Field and Lo...

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2023 International Conference on Computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Jocelyn Verna Castilani, Laurentia Alyssa Winata, Natasha Hartanti Nugraha, Nathania Christy Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The economy is one of the determinants of how a person can live their life. In this current economic situation, inflation occurs everywhere, causing the prices of necessities to rise. In order to have a decent life, people must find a job with the highest possible salary to fulfill their needs. Various job industries have their salary range. Obtaining the information of salary level for the respective job is helpful for employers and employees to estimate the expected salary. This work aims to classify the salary level of jobs available in Indonesia and determine whether those salaries are decent enough. The learning methods are logistic regression, decision tree, k-nearest neighbor, support vector machine, voting classifier, bagging classifier, random forest, and boosting classifier. Random Forest achieved the best result with an accuracy rate of 72%. Based on the analysis result, factors such as job field, educational background, working experience, working hours, and job location influence salary. © 2023 IEEE.

关键词： Decision trees

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Air Pollution Prediction using Random Forest Classifier: A Case Study of DKI Jakarta

Air Pollution Prediction using Random Forest Classifier: A C...

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2023 International Conference on Computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Muljana, Richie Ayuningtyas, Lintang Diah Daksa, Rayhan Prawira Djamhari, Simen Ferdinand Fiezayyan, Muhammad Ariiq Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

This research paper analyzes the Air Pollution Standard Index (APSI) in Jakarta, Indonesia, using the Random Forest Classifier (RFC). The study aims to predict the APSI level in Jakarta based on the concentrations of various pollutants, including particulate matter (PM), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). The data used in the study was collected from a public website called Jakarta Open data from January to December 2020. The SMOTE-Tomek technique was used to handle an imbalanced dataset in this work. The results show that the RFC model accurately predicts the APSI level in Jakarta with an accuracy of 95%. In addition, RFC can identify ozone (O3) and particulate matter (PM) are the most critical factors influencing the APSI level. This study provides valuable insights into the factors influencing air pollution in Jakarta and can be used to inform city decision-making regarding air quality management. This paper discusses the finding's significance and potential future research directions. © 2023 IEEE.

关键词： Carbon monoxide

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On the Continuity of Moore-Penrose Inverse of the Graph Laplacian 13

On the Continuity of Moore-Penrose Inverse of the Graph Lapl...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Lin, Tse-Yu Tsai, Yen-Lung National Taiwan University Data Science Degree Program Taipei Taiwan National Chengchi University Department of Mathematical Sciences Taipei Taiwan

ISBN: (纸本)9798350355079

Graph Laplacian is a fundamental tool in various fields such as spectral clustering, network analysis, image processing, and, deep learning recently for studying message passing in graph neural network models. To support the theoretical use of the graph Laplacian in these fields, in this work, we study the continuity of the Moore-Penrose generalized inverse of the graph Laplacian. We provide a graph-theoretical proof of this continuity in terms of the connectivity of the underlying graph associated with a given graph Laplacian matrix. © 2024 IEEE.

关键词： Laplace transforms

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Prior-Guided Parallel Residual Bi-Fusion Network in USV Obstacle Detection

Prior-Guided Parallel Residual Bi-Fusion Network in USV Obst...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Chih-Chung Yang, Sophia Hou, Xiu-Yu Jhang, Yu-An Institute of Data Science National Cheng Kung University Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Taiwan

ISBN: (纸本)9798350324174

In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime Computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F-avg score of 0.514. © 2023 IEEE.

关键词： Unmanned surface vehicles

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