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Bayesian classifier based on cancer prognostic markers using accelerated failure time model with frailty effect

Quality and Quantity 2025年 1-27页

作者： Vishwakarma, Gajendra K. Kumari, Pragya Bhattacharjee, Atanu Ong, Seng Huat Department of Mathematics & Computing Indian Institute of Technology Dhanbad Jharkhand Dhanbad 826004 India Division of Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur 56000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur 50603 Malaysia

This work presents a Bayesian classifier technique to categorize patients based on predictive biomarkers of time-to-event data utilizing the Accelerated Failure Time (AFT) model incorporating the frailty effect. Before classification, efficient and significant markers from a high-dimensional gene expression dataset need to be identified. Currently, it is an emerging area in oncology. A conventional three-step feature selection approach is introduced to select the most relevant markers from high-dimensional data. A threshold value for each selected marker is obtained using the proposed Bayesian classification procedure incorporating the AFT model with the frailty effect. The frailty effect is incorporated to account for unobserved heterogeneity in the expression values of subjects, allowing for a more accurate investigation of the risk of cancer-related outcomes. A simulation study is performed to validate the proposed classification approach, and the classification’s performance is evaluated using the Brier score, and the result indicates that it is relatively high. Application of the proposed classification technique is provided for two high-dimensional TCGA datasets of lung cancer patients. Our results provide evidence about the effect of classified gene expression values on the survival risk of lung cancer patients. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Accelerated failure time model Bayesian Cancer’s biomarker Classification Survival analysis

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Overview of the shared task on Hope Speech Detection for Equality, Diversity, and Inclusion 3

Overview of the shared task on Hope Speech Detection for Equ...

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3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Kumaresan, Prasanna Kumar Chakravarthi, Bharathi Raja Navaneethakrishnan, Subalalitha Chinnaudayar García-Cumbreras, Miguel Ángel Jiménez-Zafra, Salud María García-Díaz, José Antonio Valencia-García, Rafael Hardalov, Momchil Koychev, Ivan Nakov, Preslav García-Baena, Daniel Ponnusamy, Kishore Kumar Preston, Blake Insight SFI Research Centre for Data Analytics University of Galway Ireland Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland SRM Institute of Science and Technology Tamil Nadu India Universidad de Jaén Spain Universidad de Murcia Spain Amazon Barcelona Spain Sofia University St. Kliment Ohridski Bulgaria Mohamed bin Zayed University of Artificial Intelligence Masdar City United Arab Emirates Guru Nanak College Tamil Nadu India School of Computer Science University of Galway Ireland

ISBN: (纸本)9789544520847

Hope serves as a potent driving force that motivates individuals to persist in the face of life’s unpredictable nature. The Hope Speech poses a substantial challenge to online information credibility, mainly due to rapid content dissemination on social media. This article offers a concise overview of the"Hope Speech Detection for Equality, Diversity, and Inclusion-LT-EDIRANLP 2023" shared task1. The task’s objective is to classify social media posts as hopeful or not, with a specific focus on four languages: English, Hindi, Bulgarian, and Spanish. Numerous teams participated in the shared task, presenting a range of methodologies including machine learning techniques, transformer-based models, and resampling methods. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Learning systems

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Dual Priority Scheduling Algorithm for Wireless Rechargeable Sensor Networks 19

Dual Priority Scheduling Algorithm for Wireless Rechargeable...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Jiangyuan Wang, Xingfu Hawbani, Ammar Zhao, Liang Alsamhi, Saeed Hamood University of Science and Technology of China School of Computer Science and Technology China Shenyang Aerospace University School of Computer Science China Insight Centre for Data Analytics National University of Ireland Galway Ireland Ibb University Ibb Yemen

ISBN: (纸本)9798350346558

The development of wireless charging technology makes it real to charge the wireless sensor node by a mobile charging vehicle(MCV), which greatly solves the problem of the lifetime of the wireless sensor network. In this paper, a two-priority on-demand scheduling algorithm is proposed(TWOP) to further improve the arrival rate of packets under the premise of ensuring the maximum charging time. For enhancing the lifetime of the network, we use a partial charging strategy and quantitatively analyze the specific information of the node to determine the maximum charging time of the node under ideal conditions. In order to make up for the reduced energy utilization efficiency caused by the partial charging strategy, and at the same time to improve the packet arrival rate, we propose a second priority algorithm to adjust the charging sequence and prove the theoretical optimality. At the same time, we have done a lot of simulation experiments to verify the effectiveness of TWOP. © 2022 IEEE.

关键词： Sensor nodes

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Efficient Non-preemptive On-demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks 19

Efficient Non-preemptive On-demand Charging Scheduling Schem...

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作者： Naji, Abdulbary Wang, Xingfu Hawbani, Ammar Zhao, Liang Alsamhi, Saeed Hamood Ghannami, Aiman University of Science and Technology of China School of Computer Science and Technology China Shenyang Aerospace University School of Computer Science China National University of Ireland Insight Centre for Data Analytics Galway Ireland Ibb University Ibb Yemen

ISBN: (纸本)9798350346558

Recently, energy provisioning problem has become one of the leading research interests in Wireless Sensor Networks (WSNs). Deploying Mobile Charger Vehicles (MCVs) (e.g., robots, drones) equipped with high capacity-battery and Wireless Power Transfer (WPT) transmitter to charge the sensor nodes in the network wirelessly, is a promising solution. Existing scheduling policies mostly concentrated on charging tasks scheduling, but neglected the influences of network topology and energy threshold distribution. Furthermore, schedulability test of existing studies remain infeasible and we can't guarantee implementing them for larger set of tasks, making them unsuitable for real-time WRSNs. To fill this gap, in this paper, first, we propose hexagonal clustered-based deployment of sensor nodes and MCVs to provide large coverage and a robust connected network through alternative routes. Second, we propose an efficient Non-Preemptive Priority on-Demand Charging scheduling (NPDC) scheme for the mobile chargers to schedule the charging tasks according to Earliest Deadline First EDF algorithm. Third, we propose an energy threshold distribution and algorithm to assign threshold value for nodes and optimize the arrivals of charge requests. Fourth, we propose a traffic intensity-based partial charging distribution to minimize the dead node density. Finally, extensive simulations have been conducted to evaluate the proposed algorithms. The results demonstrate that our proposed algorithms achieve good performance in terms of queue length, dead nodes density, response time, and energy efficiency. © 2022 IEEE.

关键词： Sensor nodes

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Superradiant growth anomaly magnification in evolution of vector bosonic condensates bounded by a Kerr black hole with near-horizon reflection

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 62-81页

作者： Nayun Jia Yin-Da Guo Gui-Rong Liang Zhan-Feng Mai Xin Zhang Department of Physics Southern University of Science and TechnologyShenzhen 518055China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Institute of Frontier and Interdisciplinary Science Key Laboratory of Particle Physics and Particle Irradiation(Ministry of Education)Shandong UniversityQingdao 266237China School of Materials Science and Physics China University of Mining and TechnologyXuzhou 221116China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Ultralight vector particles can form evolving condensates around a Kerr black hole(BH)due to superradiant *** study the effect of near-horizon reflection on the evolution of this system:by matching three pieces of asymptotic expansions of the Proca equation in Kerr metric and considering the leading order in the electric mode,we present explicit analytical expressions for the corrected spectrum and the superradiant instability ***,in high-spin BH cases,we identify an anomalous situation where the superradiance rate is temporarily increased by the reflection parameter R,which also occurs in the scalar scenario,but is largely magnified in vector condensates due to a faster growth rate in dominant *** point out that the condition for the growth anomaly in the adiabatic case is that information carried per particle exceeds a certain value δ1/δN>2πκ_(B)√(1+R)/(1-R).We further construct several featured quantities to illustrate it,and formalize the anomalyinduced gravitational wave strain deformation.

关键词： superradiance ultralight boson black hole gravitational wave near-horizon reflection

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Improved Memory Type Product Estimator for Population Mean in Stratified Random Sampling Under Linear Cost Function

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SN Computer science 2023年第3期4卷 1-9页

作者： Yadav, Subhash Kumar Vishwakarma, Gajendra K. Varshney, Rahul Pal, Arun Department of Statistics School of Physical and Decision Sciences Babasaheb Bhimrao Ambedkar University Lucknow 226025 India Department of Mathematics and Computing Indian Institute of Technology Dhanbad Dhanbad 826004 India Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur 56000 Malaysia

In this article, we consider the memory type product estimator to estimate the population mean of the study variable in stratified random sampling. The suggested estimators’ bias and mean square error (MSE) expressions for the first order of approximation are derived. The proposed estimator is compared with the competing estimators under consideration and the efficiencies’ conditions are obtained. Further, we obtain the optimum allocation of samples in different strata along with MSEs. To achieve the solution of the problem, we frame our problem as AINLPP and solve through genetic programming technique. To show the efficiency of the estimator, we conduct simulation study. An empirical analysis based on real-life data is also offered to support the findings of the simulation study. The most efficient estimator is recommended for different practical applications. The suggested estimator is proven to be the best among the competing estimators. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AINLPP Genetic programming technique Linear cost function Product estimator Stratified random sampling

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Annotation Guided Generative Adversarial Networks for Image Augmentation of Age-Related Macular Degeneration 50

Annotation Guided Generative Adversarial Networks for Image ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Chandrasekaran, Raja Balpunuri, Saketh Reddy Senadheera, Isuru Nallagatla, Vishnu Sai Akula, Lokesh Praveen VelTech Rangarajan Dr Sagunthala R&D Institute of Science & Technology Avadi Chennai India Centre for Data Analytics and Cognition La Trobe Business School La Trobe University Melbourne Australia Department of Ophthalmology ACSR Government Medical College Andhra Pradesh Nellore India

ISBN: (纸本)9781665464543

Age-related macular degeneration (AMD) is a common eye disease that affects millions of people worldwide, leading to central vision loss. It mostly occurs in people over 50 years old. As per the literature, AMD diagnosis requires high-quality and diverse fundus images, which are often scarce and expensive to obtain. We propose a novel generative adversarial network (GAN) to address this challenge. It can synthesize realistic and clinically relevant images with AMD anomalies (drusen and Abnormalities of Choroidal Neovascular Membrane(CNVM)), enhancing dataset diversity and aiding automated diagnosis. Our GAN architecture is supplemented with (manual annotation guided) spatial attention maps to emphasize on anomalies guided through manually generated label maps. Further we propose 2 Siamese like Discriminators, for individual anomalies which emphasize the Generator module to generate AMD anomaly (drusen and abnormalities of CNVM) specific images. The proposed GAN leverages various loss functions, including contrastive loss and anomaly loss in the Siamese like Discriminator, and the adversarial loss in the combined model. The anomaly loss inspired by (Retinal detail Loss) measures the prominence of AMD signatures in the generated images. Our approach successfully generates synthetic images with specific AMD anomalies, aiding early diagnosis. The proposed GAN extended excellent dataset augmentation capabilities with 3% and 4% increase in diagnostic accuracy of drusen and abnormalities of CNVM, respectively. © 2024 IEEE.

关键词： Generative adversarial networks

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Research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Memento 2.0: An Improved Lifelog Search Engine for LSC'22 5

Memento 2.0: An Improved Lifelog Search Engine for LSC'22

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5th Annual Lifelog Search Challenge, LSC 2022

作者： Alam, Naushad Graham, Yvette Gurrin, Cathal Insight Centre for Data Analytics Dublin City University Dublin Ireland School of Computer Science and Statistics Trinity College Dublin Ireland School of Computing Dublin City University Dublin Ireland

ISBN: (纸本)9781450392396

In this paper, we present Memento 2.0, an improved version of our system which first participated in the Lifelog Search Challenge 2021. Memento 2.0 employs image-text embeddings derived from two CLIP models (ViT-L/14 and ResNet-50x64) and adopts a weighted ensemble approach to derive a combined final ranking. Our approach significantly improves the performance over the baseline LSC'21 system. We additionally make important updates to the system's user interface after analysing the shortcomings to make it more efficient and better suited to the needs of the Lifelog Search Challenge. © 2022 Owner/Author.

关键词： Search engines

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FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning

FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Mueller, Brianna Street, W. Nick Baek, Stephen Lin, Qihang Yang, Jingyi Huang, Yankun University of Iowa Department of Business Analytics Iowa City United States University of Virginia School of Data Science Charlottesville United States New York University Department of Information Systems New York United States Arizona State University Department of Information Systems Tempe United States

ISBN: (纸本)9798350362480

Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL paradigms face challenges due to heterogeneity in client data distributions and system capabilities. Personalized federated learning (pFL) has been proposed to mitigate these problems, but often requires a shared model architecture and a central entity for parameter aggregation, resulting in scalability and communication issues. More recently, model-heterogeneous FL has gained attention due to its ability to support diverse client models, but existing methods are limited by their dependence on a centralized framework, synchronized training, and publicly available datasets. To address these limitations, we introduce Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized pFL algorithm that supports model heterogeneity and asynchronous learning. Our approach utilizes a peer-to-peer model sharing mechanism and ensemble selection to achieve a more refined balance between local and global information. Experimental results show that FedPAE outperforms existing state-of-the-art pFL algorithms, effectively managing diverse client capabilities and demonstrating robustness against statistical heterogeneity. © 2024 IEEE.

关键词： Federated learning

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