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Implementation of Business Intelligence in data and Information for Student Admission Process in Mulia University

Implementation of Business Intelligence in Data and Informat...

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2019 International conference of Science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Mirwansyah, Dedy Riyayatsyah Sari, Dewi Wulan Department of Information System Mulia University Samarinda Indonesia

Mulia University is a private university that carries the slogan "Global Technopreneur Campus"with a total of 1631 students per August 2019. Mulia University has two campuses for learning support. The first one is Balikpapan Main Campus, and the other one is Samarinda Campus. The data to be used in this research is a prototype. Then, will be analyzed by Business Intelligent. Business intelligence is a series of processes to get information easily from large and complex data. In this research has been conducted two stages of analysis, which is data collection techniques and data analysis techniques on prospective student data. The application that is used in this research is Microsoft Power Business Intelligence. By using Microsoft Power Business Intelligent, it is expected to simplify the management of student admission reports, as well as decision making and to determine future strategies. The results of this research are data visualization information reports about student admission. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Optimal Design and Implementation of an Open-Source Emulation Platform for User-Centric Shared E-mobility Services 10th

Optimal Design and Implementation of an Open-Source Emula...

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10th International conference on Machine Learning, Optimization, and data Science, LOD 2024

作者： Shah, Maqsood H. Ding, Yue Zhu, Shaoshu Gu, Yingqi Liu, Mingming Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

ISBN: (纸本)9783031824807

With the rising concern over transportation emissions and pollution on a global scale, shared electric mobility services like E-cars, E-bikes, and E-scooters have emerged as promising solutions to mitigate these pressing challenges. However, existing shared E-mobility services exhibit critical design deficiencies, including insufficient service integration, imprecise energy consumption forecasting, limited scalability and geographical coverage, and a notable absence of a user-centric perspective, particularly in the context of multi-modal transportation. More importantly, there is no consolidated open-source platform which could benefit the E-mobility research community. This paper aims to bridge this gap by providing an open-source platform for shared E-mobility. The proposed platform, with an agent-in-the-loop approach and modular architecture, is tailored to diverse user preferences and offers enhanced customization. We demonstrate the viability of this platform by providing a comprehensive analysis for integrated multi-modal route-optimization in diverse scenarios of energy availability, user preferences and E-mobility tools placement for which we use modified Ant Colony Optimization algorithm so called Multi-Model Energy Constrained ACO (MMEC-ACO) and Q-Learning algorithms. Our findings demonstrate that Q-learning achieves significantly better performance in terms of travel time cost for more than 90% of the instances as compared to MMEC-ACO for different scenarios including energy availability, user preference and E-mobility tools distribution. For a fixed (O, D) pair, the average execution time to achieve optimal time cost solution for MMEC-ACO is less than 2 s, while Q-learning reaches an optimal time cost in 20 s on average. For a run-time of 2 s, Q-learning still achieves a better optimal time cost with a 20% reduction over MMEC-ACO’s time cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Travel time

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data Science Procedures to Aggregate Unstructured Disease data in Georeferenced Spreading analysis

Data Science Procedures to Aggregate Unstructured Disease Da...

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9th World conference on Information Systems and Technologies (WorldCIST)

作者： de Almeida, Lucas C. do Prado, Daniel S. Marques, Natalia A. de Caldas Filho, Francisco L. Martins, Lucas M. C. E. de Sousa, Rafael T., Jr. Univ Brasilia Fac Technol Elect Engn Dept BR-70910900 Brasilia DF Brazil

ISBN: (纸本)9783030726591;9783030726607

The importance of analyzing data on public health, especially on notifications of disease cases, can play an important role in the overall improvement of the framework for combating epidemics. This type of study proved to be extremely influential during the COVID-19 pandemic, which demanded rapid and specific actions for the management of different geographic regions. This work presents the design and results of a system that has made possible perform analysis of this nature even in contexts of scarcity of structured and even georeferenced data. It is hoped that the work will be an inspiration for new, more comprehensive initiatives, without implying that health professionals have an advanced knowledge about the techniques and technologies that form the basis of the platform, easily integrating with existing processes and adding value. The result was the generation of a structured and aggregated database of COVID-19 data in the city of Brasilia, Federal District, capital of Brazil. If the informs used to extract data previously provided only information about the disease situation, with the platform, they now provide a basis for complex epidemiological analyzes and even spreading animations.

关键词： data scraping data visualization Epidemic data analysis Georeferenced data

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Research on analysis and Prediction of Big data of Chinese Medicinal Materials in R+Hadoop

Research on Analysis and Prediction of Big Data of Chinese M...

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2021 International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2021

作者： Hu, Hongyan Xin, Ying Wu, Xijie Bao, Chengfeng Yang, Lingzhen Bai, Laxinamujila Inner Mongolia University for Nationalities Inner Mongolia Autonomous Region Tongliao City028000 China Inner Mongolia Research Institute of Traditional Inner Mongolia Medicine Engineering Technology Inner Mongolia Autonomous Region Tongliao City028000 China

ISBN: (纸本)9781665424905

This article analyzes the basic content and construction steps of R+Hadoop. The author combines the big data requirements of R+Hadoop Chinese medicinal materials to study big data preprocessing, data visualization processing, data modeling analysis, model evaluation and selection, model predictive analysis, system realization analysis, system test analysis, etc. The purpose of this article is to improve the reliability of big data analysis and prediction results and provide an important guarantee for the economic development of the industry. © 2021 IEEE.

关键词： Big data

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SWS: A Complexity-Optimized Solution for Spatial-Temporal Kernel Density visualization 48th

SWS: A Complexity-Optimized Solution for Spatial-Temporal Ke...

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48th International conference on Very Large data Bases, VLDB 2022

作者： Chan, Tsz Nam Lon Ip, Pak Leong Hou, U. Choi, Byron Xu, Jianliang Hong Kong Baptist University Hong Kong University of Macau SKL of Internet of Things for Smart City China

Spatial-temporal kernel density visualization (STKDV) has been extensively used in a wide range of applications, e.g., disease outbreak analysis, traffic accident hotspot detection, and crime hotspot detection. While STKDV can provide accurate and comprehensive data visualization, computing STKDV is time-consuming, which is not scalable to large-scale datasets. To address this issue, we develop a new sliding-window-based solution (SWS), which theoretically reduces the time complexity for generating STKDV, without increasing the space complexity. Moreover, we incorporate SWS with the progressive visualization framework, which can continuously output partial visualization results to users (from coarse to fine), until users satisfy the visualization. Our experimental studies on five large-scale datasets show that SWS achieves 1.71x to 24x speedup compared with the state-of-the-art methods. © 2021, VLDB Endowment. All rights reserved.

关键词： visualization

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Trends and Challenges of Multimodal Solutions for Text and Image Context Extraction

Trends and Challenges of Multimodal Solutions for Text and I...

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Electrical, Electronic and Information Sciences (eStream), Open conference of

作者： Tautvydas Kvietkauskas Pavel Stefanovič Faculty of Fundamental Science Vilnius Gediminas Technical University Vilnius Lithuania

ISBN: (数字)9798331598730

ISBN: (纸本)9798331598747

The number of published images, texts, and other information rapidly increases in today’s digital space. The ability to simultaneously process textual and visual information helps to interpret content more accurately. It enables the application of artificial intelligence in complex situations, such as contextual analysis and real-time monitoring of social networks. Today’s Large Language Models (LLMs) are based on text data, Object Recognition (OR) on visual data, and Vision-language (VL) models use text and visual data. The combinations of these different models can be used to create multimodal solutions to solve various context-extraction tasks. In such a way, this requires images and texts, which are input to the multimodal models. This paper systematically reviews the latest research on existing LLM, OR and VL models. The scientific articles were analysed on the Web of Science and Google Scholar databases. All the papers are free to access and date from 2019 to 2024. The main objective was to summarise the results of scientific research, tasks, and methods used in text, image, and image-text data analysis. The types of datasets and the language of the texts used in the research were also reviewed. Additionally, the results are useful for highlighting trends and challenges in the context extraction field that can be useful to other researchers.

关键词： visualization Analytical models Systematics Reviews Large language models Market research data models Object recognition data mining Context modeling

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Correlation analysis on Big data for Legislative Election

Correlation Analysis on Big Data for Legislative Election

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International conference on data and Software Engineering (ICoDSE) - data and Software Engineering for Supporting Sustainable Development Goals

作者： Dwiyanti, Latifa Rusmawati, Yanti Wisanggeni, Adyaksa Nugraha, Fata Bandung Inst Technol Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9781665494533

Indonesia as a democratic country holds general election such as legislative election and presidential election. The General Elections Commissions of Indonesia usually provide useful official data of the election result which grouped by the constituency as well as the province in Indonesia. Moreover, Statistics Indonesia institution has annual statistic data for each province (and in detail up to city/district level per province), such as poverty, prosperity, and education index. Here, we attempt to explore whether there is a correlation between the election result and the province statistic data that can be processed further as the side information for election stakeholders. We focus on (1) providing the platform to perform the big data analytics, (2) identification, selection, and integration of supporting data, (3) visualization of the data on a big data platform;and (4) perform the big data analysis. We use legislative election data (DPR) 2019 in West Java province combined with its demographic data and the party's ideology to perform the correlation analysis. The result shows that the implementation of big data analysis on such data can offer some side information for election stakeholders.

关键词： Election Big data analysis Correlation analysis

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Gamification as a Form of Active and Digital Learning in Accounting Education (2019 – 2024)

Gamification as a Form of Active and Digital Learning in Acc...

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International conference on ICT For Smart Society (ICISS)

作者： Naomi Madeleine Go Ignatius Edward Riantono Cadelina Cassandra Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Accounting Department School of Accounting Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Research in gamification has significantly increased over the last five years. Many have proved the positive effects of gamification usage in education, especially in fostering students' motivation and learning outcomes. However, gamification research done in the context of accounting education remains limited than research done in other similar fields. This paper uses bibliometric analysis to explore the current state of gamification research in accounting education using data gathered from Scopus and Google Scholar. Initial extraction using specified query and publication year limitation of 2019 - 2024 in Harzing's Publish or Perish yielded a grand total of 1,045 papers. The search was done on 27th of May 2024. The papers are then manually reviewed based on their language and their relevance to the topic of the paper. From the initial number of papers, only 138 papers are written in English and are actually discussing gamification usage in the context of accounting education. The metadata of these papers are then visualized using VOSviewer. While gamification in general education research has flourished, research in gamification in accounting education specifically has only been increasing by little up to 2022 before reaching the peak in 2023. Some of the keywords that have high occurrences are game-based learning, serious games, learning, higher education, and teaching. Using the density visualization from VOSviewer, research gaps were found on terms such as academic achievements, technology acceptance model, and financial accounting.

关键词： Ethics Technology acceptance model databases Education Bibliometrics data visualization Metadata Market research Internet Interviews

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Consciousness-Domain Index: a data-driven clustering-based consciousness labeling 44

Consciousness-Domain Index: a data-driven clustering-based c...

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44th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Liuzzi, Piergiuseppe De Bellis, Francesco Magliacano, Alfonso Estraneo, Anna Mannini, Andrea IRCSS Fdn Don Carlo Gnocchi Florence FI Italy Scuola Super Sant Anna Ist BioRobot Pontedera PI Italy

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Assessing consciousness results in one of the most complex neurological diagnosis. Even more complex and uncertain is prognosticating on consciousness recovery. Currently, consciousness is assessed by using a six-items scale, the Coma Recovery Scale-Revised. Namely, scores on the sub-items can individually assign or not a specific level of consciousness to a patient. In our work, by using solely the six sub-items of the CRS-R, we implemented a clustering algorithm labeling patients with the Consciousness-Domains Index (CDI) starting from a dataset of 190 patients with a Disorder of Consciousness (DoC). Then, the CDI is compared with the clinical state at admission and at six months via univariate analysis. The number of clusters best dividing the groups resulted equal to two and the most influencing sub-items resulted the visual and motor one. The CDI closely resembles the clinical state at admission (CSA) (Cohen's k = 0.85). On the other hand, when comparing CDI and CSA, a net improvement was found in the prognostic power of the neurological outcome at six months, targeted as presence/absence of a DoC (p < 0.001). data-driven techniques pave the way for automated and model-based search of prognostic factors, together with the use of such prognostic factors in multivariate prognostic models. Future works will address the external validation of the CDI, together with the inclusion of the CDI in a multivariate supervised model, in order to assess the true potential of such novel index.

关键词： visualization Biological system modeling Clustering algorithms Indexes Labeling

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TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates

TemporalUV: Capturing Loose Clothing with Temporally Coheren...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xie, You Mao, Huiqi Yao, Angela Thuerey, Nils Tech Univ Munich Dept Informat Munich Germany Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel approach to generate temporally coherent UV coordinates for loose clothing. Our method is not constrained by human body outlines and can capture loose garments and hair. We implemented a differentiable pipeline to learn UV mapping between a sequence of RGB inputs and textures via UV coordinates. Instead of treating the UV coordinates of each frame separately, our data generation approach connects all UV coordinates via feature matching for temporal stability. Subsequently, a generative model is trained to balance the spatial quality and temporal stability. It is driven by supervised and unsupervised losses in both UV and image spaces. Our experiments show that the trained models output high-quality UV coordinates and generalize to new poses. Once a sequence of UV coordinates has been inferred by our model, it can be used to flexibly synthesize new looks and modified visual styles. Compared to existing methods, our approach reduces the computational workload to animate new outfits by several orders of magnitude.

关键词： Hair visualization Computer vision Computational modeling Clothing Pipelines Stability analysis

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