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Gene Interactions in Survival Data Analysis: A Data-Driven Approach Using Restricted Mean Survival Time and Literature Mining 26th

Gene Interactions in Survival Data Analysis: A Data-Driven ...

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26th International Conference on Discovery science, DS 2023

作者： Kokošar, Jaka Špendl, Martin Zupan, Blaž Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)9783031452741

Unveiling gene interactions is crucial for comprehending biological processes, particularly their combined impact on phenotypes. Computational methodologies for gene interaction discovery have been extensively studied, but their application to censored data has yet to be thoroughly explored. Our work introduces a data-driven approach to identifying gene interactions that profoundly influence survival rates through the use of survival analysis. Our approach calculates the restricted mean survival time (RMST) for gene pairs and compares it against their individual expressions. If the interaction’s RMST exceeds that of the individual gene expressions, it suggests a potential functional association. We focused on L1000 landmark genes using TCGA na METABRIC data sets. Our findings demonstrate numerous additive and competing interactions and a scarcity of XOR-type interactions. We substantiated our results by cross-referencing with existing interactions in STRING and BioGRID databases and using large language models to summarize complex biological data. Although many potential gene interactions were hypothesized, only a fraction have been experimentally explored. This novel approach enables biologists to initiate a further investigation based on our ranked gene pairs and the generated literature summaries, thus offering a comprehensive, data-driven approach to understanding gene interactions affecting survival rates. © 2023, The Author(s).

关键词： Gene expression

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A New Paradigm in Blockchain-Based Financial Aid Distribution 1

引用

World Conference on Information Systems for Business Management, ISBM 2023

作者： Hasan Shahrukh, Md. Raisul Rahman, Md. Tabassinur Mansoor, Nafees Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka1207 Bangladesh

ISBN: (数字)9789819983490

ISBN: (纸本)9789819983483

Blockchain technology has emerged as a game-changer in a variety of industries, providing robust solutions that can supplant conventional procedures. The unique potential of this technology originates from its decentralized ledger systems, which enable enhanced security, transparency, and the validation of transactions without the need for intermediaries. Notably, the financial sector is making substantial progress toward implementing blockchain solutions for a variety of operations, including remittances, lending, and investments. The healthcare industry is simultaneously incorporating this technology into systems for managing medical records, tracing supply chains, and data management. Similarly, the capacity of blockchain to enhance transparency, traceability, and accountability is widely acknowledged in supply chain management, from the procurement of basic materials to the delivery of finished goods. Diverse industries, including real estate, energy, and government, are actively investigating the potential of blockchain to improve efficiency, security, and transparency. Notably, Hyperledger Besu, an open-source blockchain platform, is used to implement smart contracts that automate processes and reduce manual intervention along distribution pathways. This exhaustive review examines the transformative potential of blockchain technology across a variety of industries, discussing the obstacles encountered and providing key insights into future research and development directions. This paper seeks to serve as a pivotal resource for academics, industry stakeholders, and policymakers by synthesizing existing scholarly literature and shedding light on significant findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Predicting Chances of Cardiovascular Diseases Through Integration of Feature Selection and Ensemble Learning 1

引用

11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Bhardwaj, Raghav Mishra, Shashvat Gupta, Isha Paliwal, Shweta Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

Heart problems are among the top most notable problems in the field of medical science. The prediction of diseases related to heart is a peculiar challenge in the field of clinical analysis, and hence, it can be observed that heart problems have been the major death reason globally. The integration of different techniques of machine learning (ML) has proven to be an effective aid in decision making and helps in prediction from the diverse quantity of the data which is produced as a result of the observations in the field of health care. In this study, analysis of a real-time dataset has been performed using the integrated concept of feature selection. Different classifiers of ML are subject to parameter optimization, and values of classical performance measures are being observed. An interface has been designed to give user a pictorial view where the system will recommend what are the chances that an individual is suffering from heart disease. The identification of heart disease is based upon the proposed algorithm and identified features from the real-time dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiography

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An Image-Based Automated Potato Leaf Disease Detection Model 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Ghosh, Aditi Roy, Parthajit The Department of Computer Science The University of Burdwan West Bengal Purba Bardhaman713104 India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

Production loss in agriculture due to different diseases occurring in plants is a common problem. As traditional identification methods are time-consuming, it is better to have an automated model for the same. Detection of potato leaf diseases has been proposed in this research. Samples of healthy potato leaves, and two diseases early blight, and late blight have been considered. Data augmentation and image segmentation have been used to obtain better accuracy. SURF, Gabor Filter, Wavelet features, and fractal dimension of the images have been extracted as the features. A widely used Plant Village Dataset has been taken to train and test the model. The accuracy of the model is 95.97%. The model outperforms several cutting-edge models in comparison. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wavelet transforms

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Big Data Security: Attack’s Detection Methods Using Digital Forensics 1

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1st International Conference on Expert Clouds and Applications, ICOECA 2023

作者： Charan, Ch. Pradeepthi, A. Jyotsna, J. Lalith, U. Chintala, Radhika Rani Vadlamudi, Divya Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andra Pradesh Vaddeswaram India

ISBN: (数字)9789819917457

ISBN: (纸本)9789819917440

The main objective of this research is to defend big data against various cyberattacks. Today, it is commonplace to use big data security to safeguard data and analytical processes against malicious activity. Cybercriminals are increasingly using fraudulent calls, texts, and other techniques to steal users' private information. Nowadays, these attacks are increasingly frequent online. In this paper, we addressed various security technologies and strategies that are being utilized to make big data more secure from our prior research references. We also presented various digital forensics tools that may be used to identify who, where, and how an attack was conducted. The fields of data science and cybersecurity are combined in this study. Detecting attacks on big data and ensuring and mitigating data security using cybersecurity and digital forensics techniques are the key goals of this study. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital forensics

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Research on Motion Control of Underwater Robot Based on Improved Active Disturbance Rejection Control 16th

Research on Motion Control of Underwater Robot Based on Impr...

引用

16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Hua, Zhijie Ruan, Haobin Tu, Dawei Zhang, Xu Zhang, Kaiwei School of Mechatronic Engineering and Automation Shanghai University Shanghai China Wuxi Research Institute Huazhong University of Science and Technology Jiangsu China

ISBN: (纸本)9789819965007

The underwater robot based on visual guidance is disturbed by ocean currents, wind waves and other disturbances in the process of moving to the target position, which makes its motion nonlinear and uncertain. In this paper, an active disturbance rejection controller (ADRC) based on nonlinear function was proposed, which utilize the engineering practice concept of "large error, small gain" and "small error, large gain", to improve the motion control of visual-guided underwater robot. Firstly, the control model of the underwater robot is established. Next, an extended state observer based on the nfal function is designed and its convergence is verified by Lyapunov stability theory. Finally, the anti-disturbance comparison experiment of the proposed ADRC was carried out on the simulation platform, and the controller was applied to the specific practice. The experimental results show that the controller can eliminate the influence of external factors faster and has better anti-interference ability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Controllers

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Content-Adaptive Block Clustering for Improving VVC Adaptive Loop Filtering 12th

Content-Adaptive Block Clustering for Improving VVC Adaptiv...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Ye, Fan Li, Li Liu, Dong University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9783031463105

Adaptive loop filtering (ALF) is an important tool in the H.266/VVC standard, and offers significant compression efficiency gain. ALF adopts a set of linear filters to process the reconstructed samples of one picture, where the filter coefficients are adaptive to the picture, and the filter selection is based on classifying the samples at the granularity of 4 × 4 block. We investigate how to make ALF more adaptive by focusing on the sample classification. We argue that the classification may be replaced with kinds of clustering, since we perform the filter selection based on the clusters without requiring the definition of classes. Thus, we propose to cluster the samples, also at 4 × 4 block level, according to the content-based features extracted from the blocks. By an extensive set of experiments with different kinds of features, we observed that the proposed method achieves on average 0.69% BD-rate reduction than the H.266/VVC anchor on a number of 2K-resolution images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Adaptive filtering

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A Systematic Approach for Effective Apgar Score Assessment in 1 and 5 min Using Manifold Machine Learning Algorithms 1

引用

2nd International Conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Shaambhavi, S. Bhavani, G. Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India

ISBN: (数字)9789819970773

ISBN: (纸本)9789819970766

The Apgar score was first introduced by Dr. Virginia Apgar in 1952, and its mainly used to find the well-being of newborn infants by using the five important aspects such as heartbeat, reflex irritation, color, muscle tone, and respiration. Apgar was an assessment tool that is performed at 1 and 5 min of a child’s birth. There is a potential impact of the score on medical decision making and providing care to newborn infants. Since a low Apgar score results in harmful interventions. So here in this research paper, the analysis and comparison of R2 Score of various machine learning algorithm models is done. Here, this comparison mainly focuses on improving the R2 score of the Apgar value. Additionally, the use of the Apgar score is a very important subject in terms of research and discussion among healthcare professionals. The Apgar score has also been evaluated in various populations including preterm infants and newborns with congenital anomalies. This research paper has analyzed the comparison of 13 different machine learning algorithms and the R2 Score was obtained as 96.5% for the hybrid approach. So, utilizing machine learning to predict the Apgar score will increase R2 Score, boost neonatal research and help us better understand the physiology and health of newborns. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Window-Controlled Sepsis Prediction Using a Model Selection Approach 9th

Window-Controlled Sepsis Prediction Using a Model Selection...

引用

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Su, Shiyan Lan, Su Zhang, Zhicheng Zhu, Anjie The University of Queensland BrisbaneQLD4072 Australia University of Electronic Science and Technology of China Sichuan China

ISBN: (纸本)9783031466762

Sepsis is a major risk to patient in the Intensive Care Unit (ICU) and is associated with substantial treatment expenditure. As most cases of sepsis are acquired during the ICU stay, timely identification and intervention play a crucial role in enhancing the survival rate of septic patients and reducing the financial burden of treatment. Prior research has established that machine learning approaches surpass conventional scoring systems in predicting sepsis. However, these existing machine learning methodologies exhibit limitations when predicting sepsis with flexible window settings. Their performance is heavily reliant on the selection of prediction and feature windows, which restricts their practical applicability in clinical settings. This paper aims to overcome this challenge by introducing a model selection approach for sepsis prediction, utilizing a window-controlled strategy. Experimental results demonstrate that our proposed model outperforms existing models and exhibits enhanced stability across various prediction and feature windows. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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Teaching Service-Oriented Architectures Using a Two-Player Online Game 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Manoharan, Sathiamoorthy Ye, Xinfeng School of Computer Science University of Auckland Auckland New Zealand

ISBN: (数字)9783031648472

ISBN: (纸本)9783031648465

Teaching Service-Oriented Architecture (SOA) is crucial, as it imparts foundational knowledge and skills vital for modern software development and system design. SOA principles centre around decomposing a system into modular and loosely-coupled services that can be combined and reused, essential for navigating today’s complex, heterogeneous, and interconnected computing environments. This paper delineates a project-based learning approach to teaching SOA, exemplified through the development of a two-player online game as the foundational project. The course blended traditional lectures and flipped classrooms to ensure a comprehensive understanding and hands-on experience. Various scaffolding techniques, such as subdividing the project into manageable parts and providing illustrative examples, aided students in understanding and time management. The course’s success was primarily evaluated through student perceptions, which were overwhelmingly positive. Students appreciated the structured learning experience and the practical, hands-on approach to understanding SOA, reflecting the effectiveness of the course structure and content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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