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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Sardjono, Wahyu Pranata, Dewi Sagita Ramdhana, Aditya Agung Dwi BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia Mathematical & Engineering Sciences King's College London Cyber Security Studi Program Faculty of Natural Department of Informatics West Minster Strands WC2R 2LS United Kingdom University of Airlangga Master of Management Faculty of Economics and Business Surabaya60286 Indonesia

ISBN: (纸本)9798350363104

In the current day, digital transformation is imperative;both customer demand and market competitiveness are mostly driving that trend. The fourth industrial revolution has also affected the financial sector. One difficulty that has to be addressed is the desire from customers for quick and useful services. Traditional procedures need to be replaced with digital ones. Furthermore, banking organizations will have a competitive edge in the current market if they are successful in this digital transformation process. Successful transformation, however, does not happen by accident;in fact, the likelihood of completing this change is seen to be very low, necessitating rigorous planning for a company that is ready for the future. There are several components to the preparation in question, both internal and external. In this situation, the company's ability to combine and rearrange its resources quickly is essential for adapting to the ever-changing business environment. © 2024 IEEE.

关键词： Commerce

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Method to Reduce Lead-time of Business Process discovered 13

Method to Reduce Lead-time of Business Process discovered

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13th International Congress on Image and Signal Processing, BioMedical Engineering and informatics, CISP-BMEI 2020

作者： Dos Santos, Gilberto Aleces Southier, Luiz Fernando Puttow Scalabrin, Edson E. Pucpr Informatics Graduate Program Curitiba Brazil

ISBN: (纸本)9780738105451

The process mining allows to automatically discover process models from event logs. Those models provide information that allow to make many types of analysis based on models, such as: compliance verification, performance analysis and bottlenecks identification on the business process. The bottlenecks identification is highly relevant for process managers to suggest improvements. Here, the improvement which is interesting is the lead-time reduction. A double hypothesis was made. The first is that lead-time reduction goes through the identification and removal of bottlenecks at the business process. The second is that the focused intervention at the critical path allows to reduce lead-time causing small changes on the current process. The process mining allows, by using model process discovery, to obtain basic statistic information of a business process, as: minimal, maximum and average duration of each activity. The technique PERT/CPM allows to calculate for each activity the weighted average of the expected execution and the critical path. This helps to identify the relevant activity focusing on the bottlenecks of a business process. The experiments show that the method allows to reduce the lead-time of a process and indicate the degree of the resource's commitment in each of the method iterations, in terms of similarity among optimized model and original model. The experiments were conducted over real word "Hospital Billing"and "Sepsis cases"databases. © 2020 IEEE.

关键词： Compliance control

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Deepepiil13: Deep Learning for Prediction of Il-13 Inducing Epitopes Using Pre-Trained Language Models and Multi-Window Convolutional Neural Networks

SSRN

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SSRN 2024年

作者： Chuang, Cheng-Che Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

Interleukin-13 (IL-13) is a key cytokine involved in allergic inflammation and the cytokine storm associated with severe COVID-19. Identifying antigenic epitopes capable of inducing IL-13 holds therapeutic potential for developing epitope-based vaccines and immunomodulatory treatments. However, conventional methods for epitope prediction are often inefficient and inaccurate. This study presents DeepEpiIL13, a novel deep learning framework that integrates pre-trained language models and multi-window convolutional neural networks (CNNs) to rapidly and accurately predict IL-13-inducing epitopes from protein sequences. The ProtTrans pre-trained language model was employed to generate high-dimensional embeddings that capture rich contextual information from protein sequences. These embeddings were then inputted into a multi-window CNN architecture, enabling the efficient exploration of local and global sequence patterns associated with IL-13 induction. The proposed approach was rigorously evaluated using benchmark datasets and an independent SARS-CoV-2 dataset. DeepEpiIL13 achieved superior performance compared to traditional methods, with an impressive Matthews correlation coefficient (MCC) of 0.52 and an area under the receiver operating characteristic curve (AUC) of 0.86 on the benchmark dataset. On the independent SARS-CoV-2 dataset, DeepEpiIL13 demonstrated remarkable robustness, attaining an MCC of 0.63 and an AUC of 0.92, highlighting its potential applicability in the context of COVID-19 research. This study introduces a powerful deep learning framework for accurate epitope prediction, offering new avenues for the development of epitope-based vaccines and immunotherapies targeting IL-13-mediated disorders. The successful identification of IL-13-inducing epitopes paves the way for novel therapeutic interventions against allergic diseases, inflammatory conditions, and potentially severe viral infections like COVID-19. © 2024, The Authors. All rights reserved.

关键词： COVID-19

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Towards Precision Cancer Therapy: Prottrans and Multi-Window Scanning Convolutional Neural Networks Transforming Protein-Peptide Interaction Predictions

SSRN

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SSRN 2024年

作者： Van Le, The Zi Zhan, Jun Yu Ou, Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model achieves an impressive AUC of 0.855 and 0.822 on Set_1 and Set_2 datasets, outperforming previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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Development of Knowledge Management System for Determining Organizational Performances, Total Quality Management, and Culture 2

Development of Knowledge Management System for Determining O...

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2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019

作者： Abdul Aziz, R.Z. Maria, Delli Laila, Siti Nur Azima, Muhammad Fauzan Informatics Engineering Department Graduate Program Informatics and Business Institute Darmajaya Bandar Lampung Indonesia Accounting Department Graduate Program Informatics and Business Institute Darmajaya Bandar Lampung Indonesia Informatics Engineering Department Graduate Program Informatics and Business Institute Darmajaya Bandar Lampung Indonesia

Free market stipulated organizations to improve quality and competitiveness. To achieve this improvement, companies had to implement Total Quality Management so that the vision and mission of companies as one of the main competing factors in the free market were achieved. Many companies had problems in measuring and implementing the organizational performance and Total Quality Management because it needed enough budgets so that a system was needed to measure this organizational performance. The objective of this study was (1) to measure the organizational performance, (2) to measure the implementation of Total Quality Management, and (3) to measure the organizational culture by using Knowledge Management System (KMS)-based approach at Micro, Small & Medium Enterprises (MSMEs). The advantage of this study was to provide the best recommendations for companies. To develop this system, the writers conducted surveys to several MSMEs, invited experts and practitioners to acquire the knowledge, and carried out the literature study to obtain the recommendation about the best organizational culture at MSMEs. This study developed software used to measure the organizational performance, Total Quality Management, and organizational culture is seen from the Knowledge Management System (KMS) principles. The developed software had been examined to several companies and MSMEs with a very high level of accuracy in giving a recommendation. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Budget control

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Alternative Reference Samples to Improve Coding Efficiency for Parallel Intra Prediction Solutions

Alternative Reference Samples to Improve Coding Efficiency f...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Iago Storch Nuno Roma Daniel Palomino Sergio Bampi Graduate Program in Computing Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil INESC-ID Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Video Technology Research Group Graduate Program in Computing Federal University of Pelotas Pelotas Brazil

Exploring massive parallelism is a common strategy to mitigate the processing time of modern video encoding standards. Nonetheless, the existing data dependencies in some encoding tools pose difficult challenges to exploit such parallelism, especially during intra prediction, where the reconstructed adjacent blocks are used as references. Although some works made use of different reference samples to allow block-level parallelism in intra prediction, their proposals do not consider the variations caused by different bitrates, leading to some degradation in the output sequence. To deal with multiple bitrates more properly, this work proposes the application of image smoothing techniques to generate alternative reference samples that better represent the nuances of different bitrates. Experimental validations demonstrate that these improved references provide coding efficiency gains while still offering an equivalent parallelization opportunity.

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Interactive Process Drift Detection: A Framework for Visual Analysis of Process Drifts 3

Interactive Process Drift Detection: A Framework for Visual ...

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3rd International Conference on Process Mining Doctoral Consortium and Demo Track, ICPM-D 2021

作者： Sato, Denise Maria Vecino Fontana, Rafaela Mantovani Barddal, Jean Paul Scalabrin, Edson Emilio Graduate Program in Informatics Pontifícia Universidade Católica do Paraná Instituto Federal do Paraná Curitiba Brazil Department of Professional and Technological Education Universidade Federal do Paraná Curitiba Brazil Graduate Program in Informatics Pontifícia Universidade Católica do Paraná Curitiba Brazil

Interactive Process Drift Detection (IPDD) is a framework for visual analysis of process drifts. A process drift indicates a change in the process model occurred at some point in time. IPDD firstly generates process models for subparts of the event log using a sliding window approach. Then, it detects the drifts by evaluating similarity metrics calculated between adjacent process models;a difference in some of the metrics indicates a drift. The current implementation of IPDD generates the process models using the directly-follows graph (DFG) and applies two metrics: nodes and edges similarity. The user interface shows the drifts in the process models over time, allowing the user to visually understand the model changes. Also, the user can easily change the hyperparameters for the analysis and verify the results on the interface. The user interface of IPDD allows the user to evaluate the detected drifts by calculating the F-score metric, which is useful when using artificial datasets. The underlying idea is to ease the choice of a "good" value for the hyperparameter configuration, which is critical for almost any drift detection tool. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： User interfaces

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Creating Mathematical Models to Help Formulate Company Strategies for Knowledge Management System Implementation

Creating Mathematical Models to Help Formulate Company Strat...

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International Conference on Information Management and Technology (ICIMTech)

作者： Wahyu Sardjono Widhilaga Gia Perdana Dewi Sagita Pranata Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Post Graduate Program of Management Science State University of Jakarta Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster UK

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This research aims to build a mathematical model to formulate the problems of implementing knowledge management systems in companies that often face obstacles in achieving the desired objectives and goals. With increasing competition in the business sector, organizations or organizations realize the importance of utilizing the knowledge assets that reside in each individual and organization. If this knowledge can be managed optimally, it can become a competitive advantage for the company due to the emergence of innovative ideas by the concepts of knowledge management theory. By implementing optimal knowledge management (KM), companies can design innovative solutions to improve business operations and increase overall revenue. The factor analysis method will be used to find the determining factors for the success of the implementation of knowledge management systems (KMS) and the regression analysis method will also be used to form a mathematical model of several new factors which are formed as independent variables with the current level of community understanding of KMS as the dependent variable. The research results provide insight into strategies to improve success factors to successfully facilitate KMS implementation. This study contributes to existing knowledge management by providing insight into dynamics that go beyond the technical aspects of KMS, ultimately developing strategies for more effective and efficient utilization of knowledge and organizational growth and supporting competitive advantage for companies.

关键词： Performance evaluation Knowledge acquisition Collaboration Companies Knowledge management Mathematical models Regression analysis Information management Problem-solving System implementation

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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