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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

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An Agile Implementation Model for ERP 4

An Agile Implementation Model for ERP

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4th International Conference on information management and Technology, ICIMTech 2019

作者： Wijaya, Santo Fernandi Prabowo, Harjanto Kosala, Raymondus Raymond Meyliana Information Systems Department Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Management Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

The growing impact of industrial dynamics will be a challenge to increase productivity. Productivity is a demand for improving organizational performance. ERP systems are a way to increase productivity. Therefore, ERP implementation is important to ensure that ERP runs well. Critical factors in ERP implementation must get serious attention to project management. Even so, the reality of ERP implementation is slowly, even tends to experience failure, resulting in highly expensive. The previous research that carried out ERP implementation showed that the failure reached 40%, and the achievement of the benefits of ERP implementation in Indonesia is still not optimal. It can be said that ERP implementation is a complexity project. For this reason, it is necessary to consider other implementation methods that can overcome the problem in implementing ERP. An agile method is a proactive and reactive alternative to handle the problem in implementing of ERP. The development of an agile ERP model is one way to achieve success in ERP implementation. This research methodology is a survey approach to the study of literature. This research will propose an agile ERP model which will be used as an effective guide for achieving the success of ERP implementation. © 2019 IEEE.

关键词： Productivity

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Accuracy for Sentiment Analysis of Twitter Students on ELearning in Indonesia using Naive Bayes Algorithm Based on Particle Swarm Optimization 1

Accuracy for Sentiment Analysis of Twitter Students on ELear...

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1st International Conference on computer, science, Engineering and Technology, ICComSET 2018

作者： Kartiko, Mohamad Sfenrianto Master of Computer Science Postgraduate Programs STMIK Nusa Mandiri Jakarta Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Universitas Bina Nusantara Jakarta Indonesia

Students can use social media such as Twitter for online learning (E-Learning). This study aims to analyse an accuracy for the sentiments of students about E-Learning who use Indonesian on Twitter social media both positive and negative opinions. The algorithms used are Naive Bayes (NB). Then to optimize the accuracy of the calculation results, the Particle Swarm Optimization (NB-PSO) approach is used. In order to optimize accurate results, this study uses three experimental sequences (scenario 1, scenario 2, and scenario 2) for both NB and NB-PSO algorithms. Each scenario uses different positive and negative comments. The results of the experiment show that in scenario 1 an increase in accuracy is 10.00% for NB-PSO. Scenario 2 there is an increase in accuracy of 13.33% on NB-PSO. Meanwhile, in scenario 3 an increase in accuracy is 27.22% for NB-PSO. These results prove that the accuracy of NB-PSO is better than NB for all scenarios. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Students

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Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM and NB Methods

Sentiment Analysis of the Indonesian Police Mobile Brigade C...

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International Conference on Electronics Representation and Algorithm 2019, ICERA 2019

作者： Pratama, Bryan Saputra, Dedi Dwi Novianti, Deny Purnamasari, Endah Putri Kuntoro, Antonius Yadi Hermanto Gata, Windu Wardhani, Nia K Sfenrianto, Sfenrianto Budilaksono, Sularso Master of Computer Science-Postgraduate Program STMIK Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program Universitas Mercubuana Jakarta Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Graduate School Master's Degree Computer Science Universitas Persada Indonesia YAI. Jakarta Indonesia

The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword "Brimob" with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%. © Published under licence by IOP Publishing Ltd.

关键词： Law enforcement

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An approach for crop recommendation with uncertainty quantification based on machine learning for sustainable agricultural decision-making

Results in Engineering

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Results in Engineering 2025年 26卷

作者： Md. Sakib Bin Alam Vatcharaporn Esichaikul Aiman Lameesa Shams Forruque Ahmed Amir H. Gandomi Department of Information Technology University of Information Technology and Sciences (UITS) Dhaka 1212 Bangladesh Computer Science and Information Management Program School of Engineering and Technology Asian Institute of Technology Pathum Thani 12120 Thailand Department of Computer Science American International University-Bangladesh Dhaka 1229 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya 47500 Selangor Darul Ehsan Malaysia Department of Mathematics & Physics North South University Dhaka 1229 Bangladesh Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University 1034 Budapest Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

While machine learning (ML) models for crop recommendation have demonstrated high predictive accuracy, a critical gap persists in their practical reliability: the omission of uncertainty quantification. Existing studies predominantly deliver deterministic recommendations, neglecting inherent uncertainties arising from data noise. This raises concerns about the reliability of the decision support systems for crop recommendation. To address this, we propose an ensemble ML framework incorporating entropy-based uncertainty quantification. Trained on a publicly available Indian agricultural dataset with 2200 samples across seven agro-climatic features (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and 22 crop classes, the model achieves a predictive accuracy of 99.54 %. By estimating prediction confidence using entropy, the framework offers probabilistic recommendations that support environmentally informed decision-making under uncertainty. These findings suggest that integrating uncertainty measures into ML-driven crop recommendation systems can enhance reliability and promote sustainable agricultural practices.

关键词： Crop recommendation Machine learning Uncertainty Precision agriculture

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A novel evolution-based recommendation system 12

A novel evolution-based recommendation system

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12th International Conference on Ubi-Media Computing, Ubi-Media 2019

作者： Chen, Yi-Cheng Chu, Yen-Lung Hui, Lin Chen, Sheng-Chih Thaipisutikul, Tipajin Weng, Kai-Ze Department of Information Management National Central University Taiwan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Innovative Information and Technology Tamkang University Taiwan Master's Program of Digital Content and Technologies College of Communication National ChengChi University Taiwan Department of Computer Science and Information Engineering National Central University Taiwan

ISBN: (纸本)9781728128207

Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people's preferences usually vary with time;the traditional MF-based methods could not properly capture the change of users' interests. In this paper, by incorporating the recurrent neural network (RNN) into MF, we develop a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. A learning model is proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct the experiments on real world dataset to demonstrate the practicability. © 2019 IEEE.

关键词： Recommender systems

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A System Dynamics computer Model to Assess the Effects of Action Games on the Development of Children with Special Need 4

A System Dynamics Computer Model to Assess the Effects of Ac...

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4th International Conference on information management and Technology, ICIMTech 2019

作者： Satria, Thomas Galih Sardjono, Wahyu Panggabean, Francisco Maruli Game Application Technology Program School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Special needs children have a tendency to play more games than children with development in general. Game play activities are often carried out by children with special needs can be a support learning and therapy due to his players the game has interaction without having to meet with his co-star in person. Starting from these data, this study aims to create a model of the System Dynamics approach. Data collected will be simulated by variables other support. The simulation results of the model will be seen autistic children therapy behavior. The results are then analyzed to create an alternative to the existing model. The benefit of this research is to know the description of game play to treatment of children with special needs. The results of simulation showed that playing games in an average time children with autism play therapy can help improve the quality of children with autism if children with autism play the game in a long time actually showed a decrease. © 2019 IEEE.

关键词： System theory

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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"So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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International Journal of information management 2023年 71卷

作者： Dwivedi, Yogesh K. Kshetri, Nir Hughes, Laurie Slade, Emma Louise Jeyaraj, Anand Kar, Arpan Kumar Baabdullah, Abdullah M. Koohang, Alex Raghavan, Vishnupriya Ahuja, Manju Albanna, Hanaa Albashrawi, Mousa Ahmad Al-Busaidi, Adil S. Balakrishnan, Janarthanan Barlette, Yves Basu, Sriparna Bose, Indranil Brooks, Laurence Buhalis, Dimitrios Carter, Lemuria Chowdhury, Soumyadeb Crick, Tom Cunningham, Scott W. Davies, Gareth H. Davison, Robert M. Dé, Rahul Dennehy, Denis Duan, Yanqing Dubey, Rameshwar Dwivedi, Rohita Edwards, John S. Flavián, Carlos Gauld, Robin Grover, Varun Hu, Mei-Chih Janssen, Marijn Jones, Paul Junglas, Iris Khorana, Sangeeta Kraus, Sascha Larsen, Kai R. Latreille, Paul Laumer, Sven Malik, F. Tegwen Mardani, Abbas Mariani, Marcello Mithas, Sunil Mogaji, Emmanuel Nord, Jeretta Horn O'Connor, Siobhan Okumus, Fevzi Pagani, Margherita Pandey, Neeraj Papagiannidis, Savvas Pappas, Ilias O. Pathak, Nishith Pries-Heje, Jan Raman, Ramakrishnan Rana, Nripendra P. Rehm, Sven-Volker Ribeiro-Navarrete, Samuel Richter, Alexander Rowe, Frantz Sarker, Suprateek Stahl, Bernd Carsten Tiwari, Manoj Kumar van der Aalst, Wil Venkatesh, Viswanath Viglia, Giampaolo Wade, Michael Walton, Paul Wirtz, Jochen Wright, Ryan Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Fabian Bay Wales Swansea United Kingdom Maharashtra Pune India Bryan School of Business and Economics University of North Carolina at Greensboro United States University of Bristol Business School University of Bristol BS8 1SD United Kingdom Professor of Information Systems Raj Soin College of Business Wright State University 3640 Colonel Glenn Highway DaytonOH45435 United States School of Artificial Intelligence Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Studies Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia School of Computing Middle Georgia State University MaconGA United States Client Advisory and Transformation Stackroute NIIT Limited India Department of Information Systems Analytics and Operations College of Business University of Louisville United States Northumbria University London United Kingdom IRC-FDE KFUPM Saudi Arabia ISOM KFUPM Business School KFUPM Saudi Arabia Innovation and Technology Transfer Center Sultan Qaboos University Oman Department of Business Communication Sultan Qaboos University Oman Department of Management Studies National Institute of Technology Tiruchirappalli India Montpellier France FORE School of Management New Delhi India Indian Institute of Management Ahmedabad Vastrapur Ahmedabad380015 India Information School University of Sheffield United Kingdom Bournemouth University Business School Poole United Kingdom School of Information Systems and Technology Management University of New South Wales Sydney Australia Information Operations and Management Sciences Department TBS Business School 1 Place Alphonse Jourdain Toulouse31068 France Department of Education & Childhood Studies Swansea Univers

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics;digital transformation of organisations and societies;and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI;examining biases of generative AI attributable to training datasets and processes;exploring business and societal contexts best suited for generative AI implementation;determining optimal combinations of human and generative AI for various tasks;identifying ways to assess accuracy of text produced by generative AI;and uncovering the ethical and legal issues in using generative AI across different contexts. © 2023 The Authors

关键词： Marketing

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Constructing Prediction Model of Lung Cancer Treatment Survival 6th

Constructing Prediction Model of Lung Cancer Treatment Survi...

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6th International Conference on Frontier Computing, FC 2018

作者： Lee, Hsiu-An Chao, Louis R. Rau, Hsiao-Hsien Hsu, Chien-Yeh Department of Computer Science and Information Engineering Tamkang University Taipei Taiwan Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (纸本)9789811336478

According to statistics of the Ministry of Health and Welfare (Taiwan) in 2016, the first of Top Ten causes of death was cancer. In this category, percentages of trachea, bronchus and lung cancer were the highest. Thus, it is particularly important to study the disease. Coverage rate of national health insurance in Taiwan is up to 99.6%. Database of the national health insurance includes insurance information of the public. It is currently the mostly complete, mature and continuous clinical medial empirical data. With precise data and sufficient samples, they become the most appropriate and precise data for prediction of effectiveness of clinical illness treatment and comorbidity analysis. This study focuses on non-small cell lung cancer. It obtains cancer registration files from two million files in database of national health insurance, and screens patients who have suffered from ICD C33-34 "trachea, bronchus and lung cancer". It then connects with OPDTE of outpatient services of national health insurance to include factors of comorbidity of disease in model design and treat DEATH as analytical figure after treatment and prognosis. By linear and non-linear data mining, it studies effects of different therapies on survival rate of lung cancer patients in three years and establishes prediction model. Through Artificial Neural Network (ANN) and Logistic Regression (LR), this study successfully establishes prediction model of survival rate of treatment of non-small cell lung cancer. When the best point of LR model: Sensitivity = 77.3 and Specificity = 73.8, it includes 81% (AUROC, Area Under ROC curve) of model precision rate. When the best point of ANN model: Sensitivity = 77.6 and Specificity = 76.8, it can include 83% (AUROC) model precision rate. It shows that model precision rate of ANN is higher than logistic regression model. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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