In this paper, a combination of breast cancer treatment procedures is considered, and its impact on breast cancer survival is precisely observed. Both statistical and neural network procedures are used to predict the breast cancer survival time. The results indicate that treatment procedures that use surgical options improve breast cancer survival. In the case of non-surgical options, hormone therapy appears to be the best. Additionally, the results suggest that radiation and chemotherapy combination lead to lower survival rates. The dataset used in this research had limited cases where the chemotherapy option was prescribed. Chemotherapy alone was a confounding cancer treatment option for non-node-positive cancer. For node-positive cancer cases, chemotherapy seems to work best where the surgery option is not considered or is viable. The experiments with neural networks show that neural networks can help predict the event of death, but these techniques could not accurately predict the length of survival. Received: 30 September 2024 | Revised: 2 December 2024 | Accepted: 30 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of the study are openly available in GitHub at https://github.com/Parag8219/BreastCancerSurvival/blob/main/GHdata.csv. Author Contribution Statement Parag C. Pendharkar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. James A. Rodger: Investigation, Resources, Data curation.

This paper explores the pervasive challenges of pedestrian positioning using smartphones in densely populated urban environments where Global Navigation Satellite System (GNSS) signals are inaccessible, for example, in indoor areas. Existing sensor-based positioning methods, such as inertial navigation systems (INS), GNSS, and visual-inertial odometry (VIO), suffer from inherent restrictions that compromise the accuracy and reliability of the positioning performance. An approach based on machine learning is proposed to address these limitations, employing the Support Vector Machine (SVM) algorithm to accurately distinguish indoor/outdoor (IO) based on the measurement of GNSS. The proposed approach in this study seamlessly incorporates 3D mapping aided (3DMA) GNSS measurements and localized estimations derived by VIO via factor graph optimization (FGO), complemented by an IO detection switch, to achieve accurate pose estimation and effectively eliminate global drift. The system's effectiveness and robustness are rigorously assessed through comprehensive extensive real-life experiments, with an average reduction of 4 meters, leading to noteworthy and statistically significant findings. Received: 1 April 2024 | Revised: 13 September 2024 | Accepted: 26 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The source code that supports the findings of this study is openly available in 3DMAGNSSVINS-IOFGO at https://github.com/queenie-ho/3DMAGNSSVINS-IOFGO. Author Contribution Statement Hiu-Yi Ho and Hoi-Fung Ng: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Weisong Wen: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition. Yanlei Gu: Resources, Writing - review & editing, Writing - review & editing. Li-Ta Hsu: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition.

Multi-tiered persistent storage systems integrate many types of persistent storage devices, such as different types of NVMes, SSDs, and HDDs. This integration provides a multi-level view of persistent storage, where each tier has a different data transmission speed and capacity. Data transfer processes operating on multi-tiered persistent storage allow for the parallelisation of data transfers among the partitions of data at the same or different tiers. This work considers the problem of efficient scheduling of parallel data transfers between the tiers of persistent storage. We consider a data processing model where several data transfer processes move or copy data from one tier to another through the buffers in transient memory. We propose a new model for data processing over multi-tiered persistent storage and new algorithms to minimise both the overall time spent on parallel data transfers and the idle time of data transfer processes. We also describe how the scheduling algorithms dynamically apply different procedures to assign data transfers to the processes. Finally, we present the outcomes from the experiments that confirm the correctness and efficiency of the scheduling algorithms. Received: 15 January 2024 | Revised: 5 April 2024 | Accepted: 6 May 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Nan Noon Noon: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization, Project administration. Janusz Getta and Tianbing Xia: Conceptualization, Methodology, Formal analysis, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.

Relation classification is one of the important tasks in natural language processing, which aims to determine the relationship between entities given a sentence and their respective positions. Most of the existing methods for relation classification are based on neural networks using pre-trained models such as BERT. In recent years, models based on pre-trained models like BERT have achieved excellent results in relation classification tasks in general domains. However, these methods often struggle when applied to specialized domains due to the limitations of the corpora used for BERT pre-training. Most pre-trained models are trained on text corpora from general sources like Wikipedia, which cover a wide range of domains. As a result, the content of these corpora in specific domains is limited and lacks the necessary expertise, leading to subpar performance of relation classification models in specific domains. While providing a large number of domain-specific corpora to pre-trained models could potentially address this issue, it comes with limitations such as increased computational requirements and insufficient training of specialized vocabulary. This paper proposes a method inspired by the K-BERT pre-training model to incorporate triplet knowledge from domain knowledge graphs into sentence sequences. The triplets are transformed into sentence trees and then fed into the BERT pre-trained model using absolute and relative indices. Our model has achieved an accuracy of 93.06%, which is markedly higher than that of any other baseline approach. This approach allows the incorporation of domain knowledge without significantly increasing the computational complexity. Additionally, the paper introduces a partial input method that enables the computer to understand input sentences from multiple dimensions and hierarchical levels. Experimental results on a medical domain dataset for relation classification, which includes type labels, demonstrate significant advantages over other relation classification models in terms of Accuracy. Received: 24 January 2024 | Revised: 19 March 2024 | Accepted: 3 April 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in [Github] at https://github.com/Soso6666/Classification-data. Author Contribution Statement Boxuan Chen: Methodology, Software, Validation, Resources, Data curation, Writing - original draft, Visualization. Guan Yuan: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - review & editing, Supervision, Project administration.

Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques, customer churn prediction is a prominent one. In order to forecast customer switching, data scientists employ a variety of demographic, social, transactional, and behavioral variables and attributes. Unfortunately, many businesses in the United Kingdom still lack the comprehensive and adaptable consumer data required to perform accurate analyses. As a result, they often rely heavily on data produced by enterprise resource planning systems, which is primarily transactional in nature. Consequently, businesses are often limited to modeling and forecasting on transactional data alone and are unlikely to invest significantly in marketing research or other customer-related sources. Businesses are often limited to performing modeling and forecasting on transactional data that are most often not based on advanced techniques like recency, frequency and monetary (RFM) and ML. So, the major objective of the current work is to provide a mix of ML and RFM analysis techniques for churn prediction using mostly transactional data. The dataset was taken from the dataset search website containing online retail datasets. Every customer's RFM scores are computed based on the available data. A churn metric that indicates whether or not the customer has made a transaction in a limited time. Through this paper, different techniques are compared. We used K-means and DBSCAN clustering. By the end of this paper, it may be inferred that the act of dividing customers into six distinct clusters is a more practical and straightforward approach. Received: 29 June 2023 | Revised: 21 August 2023 | Accepted: 6 September 2023 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in [Google Drive] at https://drive.google.com/file/d/1qme8WeYkmXWfWkLa87jG37owWPCsNY65/view?usp=drive_web

In this paper we study the personalized book recommender system in a child–robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. The feedback is processed by converting the user's reaction into accept or reject. According to children's fuzzy language input, the proposed method gives the predicted interests, which is based on a common set of interests related to children's literature. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Additional phrases are extended to broaden the user's search range. Experimental results show that the proposed recommender system has an improved performance compared with the baseline results. Due to the algorithm efficiency design, it can operate on embedded consumer devices with limited computational resources. Received: 15 March 2023 | Revised: 11 April 2023 | Accepted: 28 April 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are not publicly available due to involvement with commercial products. Author Contribution Statement Yun Liu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Tianmeng Gao: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Baolin Song: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Chengwei Huang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.